Overview

Dataset statistics

Number of variables89
Number of observations119088
Missing cells2212522
Missing cells (%)20.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory80.9 MiB
Average record size in memory712.0 B

Variable types

Text1
Numeric12
Categorical76

Alerts

YEAR_WAVE has constant value ""Constant
MH7B is highly overall correlated with age_mh and 2 other fieldsHigh correlation
MH7B_2 is highly overall correlated with age_mh and 1 other fieldsHigh correlation
W28 is highly overall correlated with W27High correlation
age_mh is highly overall correlated with MH7B and 3 other fieldsHigh correlation
Age is highly overall correlated with MH7B and 4 other fieldsHigh correlation
Global11Regions is highly overall correlated with wbiHigh correlation
W3 is highly overall correlated with EducationHigh correlation
W13 is highly overall correlated with W14 and 1 other fieldsHigh correlation
W14 is highly overall correlated with W13High correlation
W15 is highly overall correlated with W13High correlation
MH7A is highly overall correlated with MH7B and 19 other fieldsHigh correlation
MH7C is highly overall correlated with MH7AHigh correlation
MH8A is highly overall correlated with MH7A and 1 other fieldsHigh correlation
MH8B is highly overall correlated with MH7A and 1 other fieldsHigh correlation
MH8C is highly overall correlated with MH7A and 1 other fieldsHigh correlation
MH8D is highly overall correlated with MH7A and 1 other fieldsHigh correlation
MH8E is highly overall correlated with MH7A and 1 other fieldsHigh correlation
MH8F is highly overall correlated with MH7A and 1 other fieldsHigh correlation
MH8G is highly overall correlated with MH7A and 1 other fieldsHigh correlation
MH8H is highly overall correlated with MH7A and 1 other fieldsHigh correlation
MH9A is highly overall correlated with MH7A and 1 other fieldsHigh correlation
MH9B is highly overall correlated with MH7A and 1 other fieldsHigh correlation
MH9C is highly overall correlated with MH7A and 1 other fieldsHigh correlation
MH9D is highly overall correlated with MH7A and 1 other fieldsHigh correlation
MH9E is highly overall correlated with MH7A and 1 other fieldsHigh correlation
MH9F is highly overall correlated with MH7A and 1 other fieldsHigh correlation
MH9G is highly overall correlated with MH7A and 1 other fieldsHigh correlation
MH9H is highly overall correlated with MH7A and 1 other fieldsHigh correlation
W27 is highly overall correlated with W28 and 1 other fieldsHigh correlation
W29 is highly overall correlated with W27High correlation
WP21758 is highly overall correlated with WP21759 and 2 other fieldsHigh correlation
WP21759 is highly overall correlated with WP21758 and 2 other fieldsHigh correlation
WP21760 is highly overall correlated with WP21758 and 2 other fieldsHigh correlation
WP21761 is highly overall correlated with WP21758 and 2 other fieldsHigh correlation
age_var1 is highly overall correlated with Age and 2 other fieldsHigh correlation
age_var2 is highly overall correlated with Age and 2 other fieldsHigh correlation
age_var3 is highly overall correlated with Age and 2 other fieldsHigh correlation
Education is highly overall correlated with W3High correlation
wbi is highly overall correlated with Global11RegionsHigh correlation
W13 is highly imbalanced (57.2%)Imbalance
MH4A is highly imbalanced (54.1%)Imbalance
MH8C is highly imbalanced (52.6%)Imbalance
W27 is highly imbalanced (55.4%)Imbalance
W3 has 4734 (4.0%) missing valuesMissing
W5B has 8522 (7.2%) missing valuesMissing
W7C has 3015 (2.5%) missing valuesMissing
W14 has 18707 (15.7%) missing valuesMissing
W15 has 18707 (15.7%) missing valuesMissing
W15_1A has 6517 (5.5%) missing valuesMissing
W15_1B has 3502 (2.9%) missing valuesMissing
W15_1C has 3502 (2.9%) missing valuesMissing
W15_1D has 3502 (2.9%) missing valuesMissing
W15_1E has 6517 (5.5%) missing valuesMissing
W15_2A has 6517 (5.5%) missing valuesMissing
W15_2B has 6517 (5.5%) missing valuesMissing
MH4A has 2013 (1.7%) missing valuesMissing
MH4B has 2013 (1.7%) missing valuesMissing
MH7B has 95117 (79.9%) missing valuesMissing
MH7B_2 has 117961 (99.1%) missing valuesMissing
MH7C has 95117 (79.9%) missing valuesMissing
MH8A has 95117 (79.9%) missing valuesMissing
MH8B has 95412 (80.1%) missing valuesMissing
MH8C has 95117 (79.9%) missing valuesMissing
MH8D has 95117 (79.9%) missing valuesMissing
MH8E has 95117 (79.9%) missing valuesMissing
MH8F has 95117 (79.9%) missing valuesMissing
MH8G has 95117 (79.9%) missing valuesMissing
MH8H has 95117 (79.9%) missing valuesMissing
MH9A has 108834 (91.4%) missing valuesMissing
MH9B has 108906 (91.5%) missing valuesMissing
MH9C has 100289 (84.2%) missing valuesMissing
MH9D has 107957 (90.7%) missing valuesMissing
MH9E has 101787 (85.5%) missing valuesMissing
MH9F has 106877 (89.7%) missing valuesMissing
MH9G has 104221 (87.5%) missing valuesMissing
MH9H has 101604 (85.3%) missing valuesMissing
W28 has 22008 (18.5%) missing valuesMissing
W29 has 22008 (18.5%) missing valuesMissing
W30 has 21082 (17.7%) missing valuesMissing
WP21757 has 7522 (6.3%) missing valuesMissing
WP21758 has 6515 (5.5%) missing valuesMissing
WP21759 has 6515 (5.5%) missing valuesMissing
WP21760 has 6515 (5.5%) missing valuesMissing
WP21761 has 6515 (5.5%) missing valuesMissing
WP21768 has 5507 (4.6%) missing valuesMissing
age_mh has 95121 (79.9%) missing valuesMissing
WPID_RANDOM has unique valuesUnique

Reproduction

Analysis started2023-10-05 04:43:18.489784
Analysis finished2023-10-05 04:44:56.299818
Duration1 minute and 37.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct113
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
2023-10-05T00:44:56.615158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length7.8251881
Min length4

Characters and Unicode

Total characters931886
Distinct characters48
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States
ValueCountFrequency (%)
china 3502
 
2.5%
india 3045
 
2.2%
united 3003
 
2.2%
south 2013
 
1.5%
russia 2002
 
1.4%
republic 2000
 
1.4%
israel 1063
 
0.8%
lebanon 1035
 
0.7%
montenegro 1027
 
0.7%
uganda 1027
 
0.7%
Other values (118) 118455
85.7%
2023-10-05T00:44:57.062257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 146182
15.7%
i 85809
 
9.2%
n 76942
 
8.3%
e 59374
 
6.4%
o 53330
 
5.7%
r 51269
 
5.5%
l 37193
 
4.0%
t 32106
 
3.4%
s 28154
 
3.0%
u 28078
 
3.0%
Other values (38) 333449
35.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 774630
83.1%
Uppercase Letter 138172
 
14.8%
Space Separator 19084
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 146182
18.9%
i 85809
11.1%
n 76942
9.9%
e 59374
 
7.7%
o 53330
 
6.9%
r 51269
 
6.6%
l 37193
 
4.8%
t 32106
 
4.1%
s 28154
 
3.6%
u 28078
 
3.6%
Other values (16) 176193
22.7%
Uppercase Letter
ValueCountFrequency (%)
C 14566
 
10.5%
S 13068
 
9.5%
M 11071
 
8.0%
I 10152
 
7.3%
B 10046
 
7.3%
A 8041
 
5.8%
N 8028
 
5.8%
U 7033
 
5.1%
K 7019
 
5.1%
E 6022
 
4.4%
Other values (11) 43126
31.2%
Space Separator
ValueCountFrequency (%)
19084
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 912802
98.0%
Common 19084
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 146182
16.0%
i 85809
 
9.4%
n 76942
 
8.4%
e 59374
 
6.5%
o 53330
 
5.8%
r 51269
 
5.6%
l 37193
 
4.1%
t 32106
 
3.5%
s 28154
 
3.1%
u 28078
 
3.1%
Other values (37) 314365
34.4%
Common
ValueCountFrequency (%)
19084
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 931886
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 146182
15.7%
i 85809
 
9.2%
n 76942
 
8.3%
e 59374
 
6.4%
o 53330
 
5.7%
r 51269
 
5.5%
l 37193
 
4.0%
t 32106
 
3.4%
s 28154
 
3.0%
u 28078
 
3.0%
Other values (38) 333449
35.8%

WPID_RANDOM
Real number (ℝ)

UNIQUE 

Distinct119088
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6118757 × 108
Minimum1.111125 × 108
Maximum2.1111081 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.5 KiB
2023-10-05T00:44:57.181286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.111125 × 108
5-th percentile1.1621179 × 108
Q11.3618468 × 108
median1.6120032 × 108
Q31.8625975 × 108
95-th percentile2.0601804 × 108
Maximum2.1111081 × 108
Range99998309
Interquartile range (IQR)50075069

Descriptive statistics

Standard deviation28858081
Coefficient of variation (CV)0.17903415
Kurtosis-1.20045
Mean1.6118757 × 108
Median Absolute Deviation (MAD)25042601
Skewness-0.0047434361
Sum1.9195506 × 1013
Variance8.3278881 × 1014
MonotonicityNot monotonic
2023-10-05T00:44:57.299312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178216898 1
 
< 0.1%
207473984 1
 
< 0.1%
210893531 1
 
< 0.1%
141365938 1
 
< 0.1%
115570178 1
 
< 0.1%
132885544 1
 
< 0.1%
180329118 1
 
< 0.1%
200156619 1
 
< 0.1%
115294635 1
 
< 0.1%
160578076 1
 
< 0.1%
Other values (119078) 119078
> 99.9%
ValueCountFrequency (%)
111112499 1
< 0.1%
111114368 1
< 0.1%
111114766 1
< 0.1%
111114849 1
< 0.1%
111115538 1
< 0.1%
111115782 1
< 0.1%
111116080 1
< 0.1%
111117965 1
< 0.1%
111118457 1
< 0.1%
111119071 1
< 0.1%
ValueCountFrequency (%)
211110808 1
< 0.1%
211110363 1
< 0.1%
211110269 1
< 0.1%
211108210 1
< 0.1%
211107076 1
< 0.1%
211103978 1
< 0.1%
211103785 1
< 0.1%
211103321 1
< 0.1%
211102870 1
< 0.1%
211101990 1
< 0.1%

WGT
Real number (ℝ)

Distinct59291
Distinct (%)49.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1
Minimum0.065293775
Maximum8.6686735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.5 KiB
2023-10-05T00:44:57.413344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.065293775
5-th percentile0.1545465
Q10.35001188
median0.66677669
Q31.2912352
95-th percentile3.1023262
Maximum8.6686735
Range8.6033797
Interquartile range (IQR)0.94122328

Descriptive statistics

Standard deviation0.94422105
Coefficient of variation (CV)0.94422105
Kurtosis3.3273567
Mean1
Median Absolute Deviation (MAD)0.38212204
Skewness1.7877271
Sum119088
Variance0.89155338
MonotonicityNot monotonic
2023-10-05T00:44:57.527364image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1393585 425
 
0.4%
0.081489987 419
 
0.4%
0.145730548 217
 
0.2%
4.889399238 180
 
0.2%
0.262941185 176
 
0.1%
0.167062088 164
 
0.1%
0.165773075 156
 
0.1%
0.158945655 152
 
0.1%
0.318024303 152
 
0.1%
0.132564166 152
 
0.1%
Other values (59281) 116895
98.2%
ValueCountFrequency (%)
0.065293775 96
0.1%
0.065310988 2
 
< 0.1%
0.065702165 1
 
< 0.1%
0.066202721 1
 
< 0.1%
0.067202571 2
 
< 0.1%
0.067525981 103
0.1%
0.068495063 1
 
< 0.1%
0.068638357 1
 
< 0.1%
0.068712894 2
 
< 0.1%
0.06943014 18
 
< 0.1%
ValueCountFrequency (%)
8.66867347 10
 
< 0.1%
7.52431709 1
 
< 0.1%
7.343562375 1
 
< 0.1%
7.144711593 1
 
< 0.1%
7.02932151 1
 
< 0.1%
6.628314301 1
 
< 0.1%
6.205425405 1
 
< 0.1%
5.635949879 21
< 0.1%
5.568736276 48
< 0.1%
5.554008666 1
 
< 0.1%

PROJWT
Real number (ℝ)

Distinct59304
Distinct (%)49.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42962.607
Minimum62.556813
Maximum1729254.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.5 KiB
2023-10-05T00:44:57.641389image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum62.556813
5-th percentile584.98035
Q12707.877
median8319.9205
Q329272.448
95-th percentile177984.41
Maximum1729254.9
Range1729192.4
Interquartile range (IQR)26564.571

Descriptive statistics

Standard deviation130262.27
Coefficient of variation (CV)3.0319918
Kurtosis74.067969
Mean42962.607
Median Absolute Deviation (MAD)6997.1634
Skewness7.6695535
Sum5.116331 × 109
Variance1.696826 × 1010
MonotonicityNot monotonic
2023-10-05T00:44:57.756414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45514.42332 419
 
0.4%
26405.6911 419
 
0.4%
7631.627981 217
 
0.2%
1584341.466 180
 
0.2%
918.8789986 176
 
0.1%
1154.138751 164
 
0.1%
2400.980185 156
 
0.1%
18839.38737 152
 
0.1%
176.0392774 152
 
0.1%
3940.25045 151
 
0.1%
Other values (59294) 116902
98.2%
ValueCountFrequency (%)
62.55681315 92
0.1%
84.85158286 1
 
< 0.1%
85.13506101 1
 
< 0.1%
87.14023511 3
 
< 0.1%
88.05621335 1
 
< 0.1%
89.04450345 9
 
< 0.1%
90.02819277 1
 
< 0.1%
90.11548863 4
 
< 0.1%
90.2138259 2
 
< 0.1%
91.03385883 7
 
< 0.1%
ValueCountFrequency (%)
1729254.924 72
 
0.1%
1721845.403 1
 
< 0.1%
1717620.4 4
 
< 0.1%
1704478.435 2
 
< 0.1%
1607714.11 2
 
< 0.1%
1603769.158 1
 
< 0.1%
1585419.133 1
 
< 0.1%
1584341.466 180
0.2%
1578564.469 1
 
< 0.1%
1574255.815 1
 
< 0.1%

FIELD_DATE
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
12/01/2020
62832 
11/01/2020
30139 
01/01/2021
15091 
10/01/2020
9021 
02/01/2021
 
2005

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1190880
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10/01/2020
2nd row10/01/2020
3rd row10/01/2020
4th row10/01/2020
5th row10/01/2020

Common Values

ValueCountFrequency (%)
12/01/2020 62832
52.8%
11/01/2020 30139
25.3%
01/01/2021 15091
 
12.7%
10/01/2020 9021
 
7.6%
02/01/2021 2005
 
1.7%

Length

2023-10-05T00:44:57.856455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:44:57.938455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
12/01/2020 62832
52.8%
11/01/2020 30139
25.3%
01/01/2021 15091
 
12.7%
10/01/2020 9021
 
7.6%
02/01/2021 2005
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 366285
30.8%
2 303013
25.4%
1 283406
23.8%
/ 238176
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 952704
80.0%
Other Punctuation 238176
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 366285
38.4%
2 303013
31.8%
1 283406
29.7%
Other Punctuation
ValueCountFrequency (%)
/ 238176
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1190880
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 366285
30.8%
2 303013
25.4%
1 283406
23.8%
/ 238176
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1190880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 366285
30.8%
2 303013
25.4%
1 283406
23.8%
/ 238176
20.0%

YEAR_WAVE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
2020
119088 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters476352
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 119088
100.0%

Length

2023-10-05T00:44:58.027488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:44:58.107501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 119088
100.0%

Most occurring characters

ValueCountFrequency (%)
2 238176
50.0%
0 238176
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 476352
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 238176
50.0%
0 238176
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 476352
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 238176
50.0%
0 238176
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 476352
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 238176
50.0%
0 238176
50.0%

W1
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
2
60006 
3
32163 
4
12715 
1
12584 
99
 
1620

Length

Max length2
Median length1
Mean length1.0136034
Min length1

Characters and Unicode

Total characters120708
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row3

Common Values

ValueCountFrequency (%)
2 60006
50.4%
3 32163
27.0%
4 12715
 
10.7%
1 12584
 
10.6%
99 1620
 
1.4%

Length

2023-10-05T00:44:58.179522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:44:58.264535image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2 60006
50.4%
3 32163
27.0%
4 12715
 
10.7%
1 12584
 
10.6%
99 1620
 
1.4%

Most occurring characters

ValueCountFrequency (%)
2 60006
49.7%
3 32163
26.6%
4 12715
 
10.5%
1 12584
 
10.4%
9 3240
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120708
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 60006
49.7%
3 32163
26.6%
4 12715
 
10.5%
1 12584
 
10.4%
9 3240
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common 120708
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 60006
49.7%
3 32163
26.6%
4 12715
 
10.5%
1 12584
 
10.4%
9 3240
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 60006
49.7%
3 32163
26.6%
4 12715
 
10.5%
1 12584
 
10.4%
9 3240
 
2.7%

W2
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
2
46624 
1
43384 
3
19051 
4
8650 
99
 
1379

Length

Max length2
Median length1
Mean length1.0115797
Min length1

Characters and Unicode

Total characters120467
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 46624
39.2%
1 43384
36.4%
3 19051
16.0%
4 8650
 
7.3%
99 1379
 
1.2%

Length

2023-10-05T00:44:58.348561image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:44:58.436585image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2 46624
39.2%
1 43384
36.4%
3 19051
16.0%
4 8650
 
7.3%
99 1379
 
1.2%

Most occurring characters

ValueCountFrequency (%)
2 46624
38.7%
1 43384
36.0%
3 19051
15.8%
4 8650
 
7.2%
9 2758
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120467
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 46624
38.7%
1 43384
36.0%
3 19051
15.8%
4 8650
 
7.2%
9 2758
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 120467
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 46624
38.7%
1 43384
36.0%
3 19051
15.8%
4 8650
 
7.2%
9 2758
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120467
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 46624
38.7%
1 43384
36.0%
3 19051
15.8%
4 8650
 
7.2%
9 2758
 
2.3%

W3
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing4734
Missing (%)4.0%
Memory size930.5 KiB
2.0
65327 
3.0
33792 
1.0
11134 
0.0
 
4101

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters343062
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 65327
54.9%
3.0 33792
28.4%
1.0 11134
 
9.3%
0.0 4101
 
3.4%
(Missing) 4734
 
4.0%

Length

2023-10-05T00:44:58.517587image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:44:58.609613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 65327
57.1%
3.0 33792
29.6%
1.0 11134
 
9.7%
0.0 4101
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 118455
34.5%
. 114354
33.3%
2 65327
19.0%
3 33792
 
9.9%
1 11134
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 228708
66.7%
Other Punctuation 114354
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118455
51.8%
2 65327
28.6%
3 33792
 
14.8%
1 11134
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 114354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 343062
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 118455
34.5%
. 114354
33.3%
2 65327
19.0%
3 33792
 
9.9%
1 11134
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 343062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 118455
34.5%
. 114354
33.3%
2 65327
19.0%
3 33792
 
9.9%
1 11134
 
3.2%

W4
Categorical

Distinct5
Distinct (%)< 0.1%
Missing1005
Missing (%)0.8%
Memory size930.5 KiB
2.0
47521 
1.0
38241 
3.0
19809 
4.0
8327 
99.0
 
4185

Length

Max length4
Median length3
Mean length3.0354412
Min length3

Characters and Unicode

Total characters358434
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 47521
39.9%
1.0 38241
32.1%
3.0 19809
16.6%
4.0 8327
 
7.0%
99.0 4185
 
3.5%
(Missing) 1005
 
0.8%

Length

2023-10-05T00:44:58.696629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:44:58.784659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 47521
40.2%
1.0 38241
32.4%
3.0 19809
16.8%
4.0 8327
 
7.1%
99.0 4185
 
3.5%

Most occurring characters

ValueCountFrequency (%)
. 118083
32.9%
0 118083
32.9%
2 47521
13.3%
1 38241
 
10.7%
3 19809
 
5.5%
9 8370
 
2.3%
4 8327
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 240351
67.1%
Other Punctuation 118083
32.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118083
49.1%
2 47521
19.8%
1 38241
 
15.9%
3 19809
 
8.2%
9 8370
 
3.5%
4 8327
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 118083
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 358434
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 118083
32.9%
0 118083
32.9%
2 47521
13.3%
1 38241
 
10.7%
3 19809
 
5.5%
9 8370
 
2.3%
4 8327
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 358434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 118083
32.9%
0 118083
32.9%
2 47521
13.3%
1 38241
 
10.7%
3 19809
 
5.5%
9 8370
 
2.3%
4 8327
 
2.3%

W5A
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
2
49252 
1
35781 
3
19699 
4
10416 
99
 
3940

Length

Max length2
Median length1
Mean length1.0330848
Min length1

Characters and Unicode

Total characters123028
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row2
4th row2
5th row4

Common Values

ValueCountFrequency (%)
2 49252
41.4%
1 35781
30.0%
3 19699
 
16.5%
4 10416
 
8.7%
99 3940
 
3.3%

Length

2023-10-05T00:44:58.871667image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:44:58.958704image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2 49252
41.4%
1 35781
30.0%
3 19699
 
16.5%
4 10416
 
8.7%
99 3940
 
3.3%

Most occurring characters

ValueCountFrequency (%)
2 49252
40.0%
1 35781
29.1%
3 19699
 
16.0%
4 10416
 
8.5%
9 7880
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123028
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 49252
40.0%
1 35781
29.1%
3 19699
 
16.0%
4 10416
 
8.5%
9 7880
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 123028
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 49252
40.0%
1 35781
29.1%
3 19699
 
16.0%
4 10416
 
8.5%
9 7880
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123028
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 49252
40.0%
1 35781
29.1%
3 19699
 
16.0%
4 10416
 
8.5%
9 7880
 
6.4%

W5B
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing8522
Missing (%)7.2%
Memory size930.5 KiB
2.0
35732 
1.0
24189 
4.0
22786 
3.0
22183 
99.0
5676 

Length

Max length4
Median length3
Mean length3.0513359
Min length3

Characters and Unicode

Total characters337374
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row3.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
2.0 35732
30.0%
1.0 24189
20.3%
4.0 22786
19.1%
3.0 22183
18.6%
99.0 5676
 
4.8%
(Missing) 8522
 
7.2%

Length

2023-10-05T00:44:59.042722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:44:59.126723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 35732
32.3%
1.0 24189
21.9%
4.0 22786
20.6%
3.0 22183
20.1%
99.0 5676
 
5.1%

Most occurring characters

ValueCountFrequency (%)
. 110566
32.8%
0 110566
32.8%
2 35732
 
10.6%
1 24189
 
7.2%
4 22786
 
6.8%
3 22183
 
6.6%
9 11352
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 226808
67.2%
Other Punctuation 110566
32.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 110566
48.7%
2 35732
 
15.8%
1 24189
 
10.7%
4 22786
 
10.0%
3 22183
 
9.8%
9 11352
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 110566
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 337374
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 110566
32.8%
0 110566
32.8%
2 35732
 
10.6%
1 24189
 
7.2%
4 22786
 
6.8%
3 22183
 
6.6%
9 11352
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 337374
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 110566
32.8%
0 110566
32.8%
2 35732
 
10.6%
1 24189
 
7.2%
4 22786
 
6.8%
3 22183
 
6.6%
9 11352
 
3.4%

W5C
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
1
44364 
2
43792 
3
14654 
99
9525 
4
6753 

Length

Max length2
Median length1
Mean length1.0799829
Min length1

Characters and Unicode

Total characters128613
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 44364
37.3%
2 43792
36.8%
3 14654
 
12.3%
99 9525
 
8.0%
4 6753
 
5.7%

Length

2023-10-05T00:44:59.217746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:44:59.303770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1 44364
37.3%
2 43792
36.8%
3 14654
 
12.3%
99 9525
 
8.0%
4 6753
 
5.7%

Most occurring characters

ValueCountFrequency (%)
1 44364
34.5%
2 43792
34.0%
9 19050
14.8%
3 14654
 
11.4%
4 6753
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 128613
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 44364
34.5%
2 43792
34.0%
9 19050
14.8%
3 14654
 
11.4%
4 6753
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 128613
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 44364
34.5%
2 43792
34.0%
9 19050
14.8%
3 14654
 
11.4%
4 6753
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 128613
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 44364
34.5%
2 43792
34.0%
9 19050
14.8%
3 14654
 
11.4%
4 6753
 
5.3%

W5D
Categorical

Distinct5
Distinct (%)< 0.1%
Missing1013
Missing (%)0.9%
Memory size930.5 KiB
2.0
48169 
3.0
28843 
1.0
18420 
4.0
15913 
99.0
6730 

Length

Max length4
Median length3
Mean length3.0569977
Min length3

Characters and Unicode

Total characters360955
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 48169
40.4%
3.0 28843
24.2%
1.0 18420
 
15.5%
4.0 15913
 
13.4%
99.0 6730
 
5.7%
(Missing) 1013
 
0.9%

Length

2023-10-05T00:44:59.397786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:44:59.499821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 48169
40.8%
3.0 28843
24.4%
1.0 18420
 
15.6%
4.0 15913
 
13.5%
99.0 6730
 
5.7%

Most occurring characters

ValueCountFrequency (%)
. 118075
32.7%
0 118075
32.7%
2 48169
13.3%
3 28843
 
8.0%
1 18420
 
5.1%
4 15913
 
4.4%
9 13460
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242880
67.3%
Other Punctuation 118075
32.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118075
48.6%
2 48169
19.8%
3 28843
 
11.9%
1 18420
 
7.6%
4 15913
 
6.6%
9 13460
 
5.5%
Other Punctuation
ValueCountFrequency (%)
. 118075
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 360955
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 118075
32.7%
0 118075
32.7%
2 48169
13.3%
3 28843
 
8.0%
1 18420
 
5.1%
4 15913
 
4.4%
9 13460
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360955
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 118075
32.7%
0 118075
32.7%
2 48169
13.3%
3 28843
 
8.0%
1 18420
 
5.1%
4 15913
 
4.4%
9 13460
 
3.7%

W5E
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
1
52915 
2
46093 
3
13578 
4
 
4243
99
 
2259

Length

Max length2
Median length1
Mean length1.0189692
Min length1

Characters and Unicode

Total characters121347
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 52915
44.4%
2 46093
38.7%
3 13578
 
11.4%
4 4243
 
3.6%
99 2259
 
1.9%

Length

2023-10-05T00:44:59.589827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:44:59.686850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1 52915
44.4%
2 46093
38.7%
3 13578
 
11.4%
4 4243
 
3.6%
99 2259
 
1.9%

Most occurring characters

ValueCountFrequency (%)
1 52915
43.6%
2 46093
38.0%
3 13578
 
11.2%
9 4518
 
3.7%
4 4243
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121347
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 52915
43.6%
2 46093
38.0%
3 13578
 
11.2%
9 4518
 
3.7%
4 4243
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 121347
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 52915
43.6%
2 46093
38.0%
3 13578
 
11.2%
9 4518
 
3.7%
4 4243
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 52915
43.6%
2 46093
38.0%
3 13578
 
11.2%
9 4518
 
3.7%
4 4243
 
3.5%

W5F
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
2
49363 
1
27327 
3
22023 
4
11396 
99
8979 

Length

Max length2
Median length1
Mean length1.075398
Min length1

Characters and Unicode

Total characters128067
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 49363
41.5%
1 27327
22.9%
3 22023
18.5%
4 11396
 
9.6%
99 8979
 
7.5%

Length

2023-10-05T00:44:59.778877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:44:59.871898image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2 49363
41.5%
1 27327
22.9%
3 22023
18.5%
4 11396
 
9.6%
99 8979
 
7.5%

Most occurring characters

ValueCountFrequency (%)
2 49363
38.5%
1 27327
21.3%
3 22023
17.2%
9 17958
 
14.0%
4 11396
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 128067
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 49363
38.5%
1 27327
21.3%
3 22023
17.2%
9 17958
 
14.0%
4 11396
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common 128067
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 49363
38.5%
1 27327
21.3%
3 22023
17.2%
9 17958
 
14.0%
4 11396
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 128067
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 49363
38.5%
1 27327
21.3%
3 22023
17.2%
9 17958
 
14.0%
4 11396
 
8.9%

W5G
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
2
35493 
4
35077 
3
25549 
1
14991 
99
7978 

Length

Max length2
Median length1
Mean length1.0669925
Min length1

Characters and Unicode

Total characters127066
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row4
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 35493
29.8%
4 35077
29.5%
3 25549
21.5%
1 14991
12.6%
99 7978
 
6.7%

Length

2023-10-05T00:44:59.960930image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:00.052932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2 35493
29.8%
4 35077
29.5%
3 25549
21.5%
1 14991
12.6%
99 7978
 
6.7%

Most occurring characters

ValueCountFrequency (%)
2 35493
27.9%
4 35077
27.6%
3 25549
20.1%
9 15956
12.6%
1 14991
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 127066
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35493
27.9%
4 35077
27.6%
3 25549
20.1%
9 15956
12.6%
1 14991
11.8%

Most occurring scripts

ValueCountFrequency (%)
Common 127066
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 35493
27.9%
4 35077
27.6%
3 25549
20.1%
9 15956
12.6%
1 14991
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127066
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 35493
27.9%
4 35077
27.6%
3 25549
20.1%
9 15956
12.6%
1 14991
11.8%

W6
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
1
49278 
2
47579 
3
11990 
99
7102 
4
 
3139

Length

Max length2
Median length1
Mean length1.0596366
Min length1

Characters and Unicode

Total characters126190
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 49278
41.4%
2 47579
40.0%
3 11990
 
10.1%
99 7102
 
6.0%
4 3139
 
2.6%

Length

2023-10-05T00:45:00.140953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:00.226984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1 49278
41.4%
2 47579
40.0%
3 11990
 
10.1%
99 7102
 
6.0%
4 3139
 
2.6%

Most occurring characters

ValueCountFrequency (%)
1 49278
39.1%
2 47579
37.7%
9 14204
 
11.3%
3 11990
 
9.5%
4 3139
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 126190
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 49278
39.1%
2 47579
37.7%
9 14204
 
11.3%
3 11990
 
9.5%
4 3139
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 126190
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 49278
39.1%
2 47579
37.7%
9 14204
 
11.3%
3 11990
 
9.5%
4 3139
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 49278
39.1%
2 47579
37.7%
9 14204
 
11.3%
3 11990
 
9.5%
4 3139
 
2.5%

W7A
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
2
49816 
1
43452 
3
13633 
99
7993 
4
 
4194

Length

Max length2
Median length1
Mean length1.0671184
Min length1

Characters and Unicode

Total characters127081
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 49816
41.8%
1 43452
36.5%
3 13633
 
11.4%
99 7993
 
6.7%
4 4194
 
3.5%

Length

2023-10-05T00:45:00.307997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:00.399012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2 49816
41.8%
1 43452
36.5%
3 13633
 
11.4%
99 7993
 
6.7%
4 4194
 
3.5%

Most occurring characters

ValueCountFrequency (%)
2 49816
39.2%
1 43452
34.2%
9 15986
 
12.6%
3 13633
 
10.7%
4 4194
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 127081
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 49816
39.2%
1 43452
34.2%
9 15986
 
12.6%
3 13633
 
10.7%
4 4194
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 127081
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 49816
39.2%
1 43452
34.2%
9 15986
 
12.6%
3 13633
 
10.7%
4 4194
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127081
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 49816
39.2%
1 43452
34.2%
9 15986
 
12.6%
3 13633
 
10.7%
4 4194
 
3.3%

W7B
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
2
49015 
1
43530 
3
14900 
99
6853 
4
 
4790

Length

Max length2
Median length1
Mean length1.0575457
Min length1

Characters and Unicode

Total characters125941
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 49015
41.2%
1 43530
36.6%
3 14900
 
12.5%
99 6853
 
5.8%
4 4790
 
4.0%

Length

2023-10-05T00:45:00.490032image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:00.579066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2 49015
41.2%
1 43530
36.6%
3 14900
 
12.5%
99 6853
 
5.8%
4 4790
 
4.0%

Most occurring characters

ValueCountFrequency (%)
2 49015
38.9%
1 43530
34.6%
3 14900
 
11.8%
9 13706
 
10.9%
4 4790
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125941
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 49015
38.9%
1 43530
34.6%
3 14900
 
11.8%
9 13706
 
10.9%
4 4790
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common 125941
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 49015
38.9%
1 43530
34.6%
3 14900
 
11.8%
9 13706
 
10.9%
4 4790
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 49015
38.9%
1 43530
34.6%
3 14900
 
11.8%
9 13706
 
10.9%
4 4790
 
3.8%

W7C
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing3015
Missing (%)2.5%
Memory size930.5 KiB
2.0
43577 
3.0
26857 
1.0
22629 
4.0
13795 
99.0
9215 

Length

Max length4
Median length3
Mean length3.0793897
Min length3

Characters and Unicode

Total characters357434
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row3.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
2.0 43577
36.6%
3.0 26857
22.6%
1.0 22629
19.0%
4.0 13795
 
11.6%
99.0 9215
 
7.7%
(Missing) 3015
 
2.5%

Length

2023-10-05T00:45:00.671074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:00.762110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 43577
37.5%
3.0 26857
23.1%
1.0 22629
19.5%
4.0 13795
 
11.9%
99.0 9215
 
7.9%

Most occurring characters

ValueCountFrequency (%)
. 116073
32.5%
0 116073
32.5%
2 43577
 
12.2%
3 26857
 
7.5%
1 22629
 
6.3%
9 18430
 
5.2%
4 13795
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 241361
67.5%
Other Punctuation 116073
32.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 116073
48.1%
2 43577
 
18.1%
3 26857
 
11.1%
1 22629
 
9.4%
9 18430
 
7.6%
4 13795
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 116073
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 357434
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 116073
32.5%
0 116073
32.5%
2 43577
 
12.2%
3 26857
 
7.5%
1 22629
 
6.3%
9 18430
 
5.2%
4 13795
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 357434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 116073
32.5%
0 116073
32.5%
2 43577
 
12.2%
3 26857
 
7.5%
1 22629
 
6.3%
9 18430
 
5.2%
4 13795
 
3.9%

W8
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
1
45266 
2
40364 
3
27340 
99
6118 

Length

Max length2
Median length1
Mean length1.0513738
Min length1

Characters and Unicode

Total characters125206
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 45266
38.0%
2 40364
33.9%
3 27340
23.0%
99 6118
 
5.1%

Length

2023-10-05T00:45:00.850130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:00.932143image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1 45266
38.0%
2 40364
33.9%
3 27340
23.0%
99 6118
 
5.1%

Most occurring characters

ValueCountFrequency (%)
1 45266
36.2%
2 40364
32.2%
3 27340
21.8%
9 12236
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125206
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 45266
36.2%
2 40364
32.2%
3 27340
21.8%
9 12236
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Common 125206
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 45266
36.2%
2 40364
32.2%
3 27340
21.8%
9 12236
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125206
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 45266
36.2%
2 40364
32.2%
3 27340
21.8%
9 12236
 
9.8%

W9
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
2
52779 
1
43555 
3
17288 
99
5466 

Length

Max length2
Median length1
Mean length1.0458988
Min length1

Characters and Unicode

Total characters124554
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 52779
44.3%
1 43555
36.6%
3 17288
 
14.5%
99 5466
 
4.6%

Length

2023-10-05T00:45:01.013150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:01.097169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2 52779
44.3%
1 43555
36.6%
3 17288
 
14.5%
99 5466
 
4.6%

Most occurring characters

ValueCountFrequency (%)
2 52779
42.4%
1 43555
35.0%
3 17288
 
13.9%
9 10932
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124554
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 52779
42.4%
1 43555
35.0%
3 17288
 
13.9%
9 10932
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common 124554
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 52779
42.4%
1 43555
35.0%
3 17288
 
13.9%
9 10932
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124554
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 52779
42.4%
1 43555
35.0%
3 17288
 
13.9%
9 10932
 
8.8%

W10
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
1
65136 
2
36011 
99
9853 
3
8088 

Length

Max length2
Median length1
Mean length1.0827371
Min length1

Characters and Unicode

Total characters128941
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row3

Common Values

ValueCountFrequency (%)
1 65136
54.7%
2 36011
30.2%
99 9853
 
8.3%
3 8088
 
6.8%

Length

2023-10-05T00:45:01.175204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:01.256222image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1 65136
54.7%
2 36011
30.2%
99 9853
 
8.3%
3 8088
 
6.8%

Most occurring characters

ValueCountFrequency (%)
1 65136
50.5%
2 36011
27.9%
9 19706
 
15.3%
3 8088
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 128941
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 65136
50.5%
2 36011
27.9%
9 19706
 
15.3%
3 8088
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common 128941
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 65136
50.5%
2 36011
27.9%
9 19706
 
15.3%
3 8088
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 128941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 65136
50.5%
2 36011
27.9%
9 19706
 
15.3%
3 8088
 
6.3%

W11A
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
1
71133 
3
26446 
2
11146 
4
 
5716
99
 
4647

Length

Max length2
Median length1
Mean length1.0390216
Min length1

Characters and Unicode

Total characters123735
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 71133
59.7%
3 26446
 
22.2%
2 11146
 
9.4%
4 5716
 
4.8%
99 4647
 
3.9%

Length

2023-10-05T00:45:01.334239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:01.421248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1 71133
59.7%
3 26446
 
22.2%
2 11146
 
9.4%
4 5716
 
4.8%
99 4647
 
3.9%

Most occurring characters

ValueCountFrequency (%)
1 71133
57.5%
3 26446
 
21.4%
2 11146
 
9.0%
9 9294
 
7.5%
4 5716
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123735
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 71133
57.5%
3 26446
 
21.4%
2 11146
 
9.0%
9 9294
 
7.5%
4 5716
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common 123735
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 71133
57.5%
3 26446
 
21.4%
2 11146
 
9.0%
9 9294
 
7.5%
4 5716
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 71133
57.5%
3 26446
 
21.4%
2 11146
 
9.0%
9 9294
 
7.5%
4 5716
 
4.6%

W11B
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
1
60845 
3
25834 
2
19174 
4
7207 
99
 
6028

Length

Max length2
Median length1
Mean length1.050618
Min length1

Characters and Unicode

Total characters125116
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1 60845
51.1%
3 25834
21.7%
2 19174
 
16.1%
4 7207
 
6.1%
99 6028
 
5.1%

Length

2023-10-05T00:45:01.503260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:01.592279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1 60845
51.1%
3 25834
21.7%
2 19174
 
16.1%
4 7207
 
6.1%
99 6028
 
5.1%

Most occurring characters

ValueCountFrequency (%)
1 60845
48.6%
3 25834
20.6%
2 19174
 
15.3%
9 12056
 
9.6%
4 7207
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 125116
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 60845
48.6%
3 25834
20.6%
2 19174
 
15.3%
9 12056
 
9.6%
4 7207
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 125116
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 60845
48.6%
3 25834
20.6%
2 19174
 
15.3%
9 12056
 
9.6%
4 7207
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 60845
48.6%
3 25834
20.6%
2 19174
 
15.3%
9 12056
 
9.6%
4 7207
 
5.8%

MH2A
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
1
64881 
2
33690 
99
8716 
3
8649 
4
 
3152

Length

Max length2
Median length1
Mean length1.0731896
Min length1

Characters and Unicode

Total characters127804
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 64881
54.5%
2 33690
28.3%
99 8716
 
7.3%
3 8649
 
7.3%
4 3152
 
2.6%

Length

2023-10-05T00:45:01.680300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:01.771320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1 64881
54.5%
2 33690
28.3%
99 8716
 
7.3%
3 8649
 
7.3%
4 3152
 
2.6%

Most occurring characters

ValueCountFrequency (%)
1 64881
50.8%
2 33690
26.4%
9 17432
 
13.6%
3 8649
 
6.8%
4 3152
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 127804
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 64881
50.8%
2 33690
26.4%
9 17432
 
13.6%
3 8649
 
6.8%
4 3152
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 127804
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 64881
50.8%
2 33690
26.4%
9 17432
 
13.6%
3 8649
 
6.8%
4 3152
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 64881
50.8%
2 33690
26.4%
9 17432
 
13.6%
3 8649
 
6.8%
4 3152
 
2.5%

MH2B
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
2
44926 
1
33445 
3
18978 
99
11603 
4
10136 

Length

Max length2
Median length1
Mean length1.0974322
Min length1

Characters and Unicode

Total characters130691
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 44926
37.7%
1 33445
28.1%
3 18978
15.9%
99 11603
 
9.7%
4 10136
 
8.5%

Length

2023-10-05T00:45:01.859346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:01.959362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2 44926
37.7%
1 33445
28.1%
3 18978
15.9%
99 11603
 
9.7%
4 10136
 
8.5%

Most occurring characters

ValueCountFrequency (%)
2 44926
34.4%
1 33445
25.6%
9 23206
17.8%
3 18978
14.5%
4 10136
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130691
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 44926
34.4%
1 33445
25.6%
9 23206
17.8%
3 18978
14.5%
4 10136
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Common 130691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 44926
34.4%
1 33445
25.6%
9 23206
17.8%
3 18978
14.5%
4 10136
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 44926
34.4%
1 33445
25.6%
9 23206
17.8%
3 18978
14.5%
4 10136
 
7.8%

W13
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
1
100381 
2
17595 
99
 
1112

Length

Max length2
Median length1
Mean length1.0093376
Min length1

Characters and Unicode

Total characters120200
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 100381
84.3%
2 17595
 
14.8%
99 1112
 
0.9%

Length

2023-10-05T00:45:02.057392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:02.177415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100381
84.3%
2 17595
 
14.8%
99 1112
 
0.9%

Most occurring characters

ValueCountFrequency (%)
1 100381
83.5%
2 17595
 
14.6%
9 2224
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120200
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 100381
83.5%
2 17595
 
14.6%
9 2224
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 120200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 100381
83.5%
2 17595
 
14.6%
9 2224
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 100381
83.5%
2 17595
 
14.6%
9 2224
 
1.9%

W14
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing18707
Missing (%)15.7%
Memory size930.5 KiB
2.0
49917 
1.0
25518 
3.0
21131 
4.0
 
3111
99.0
 
704

Length

Max length4
Median length3
Mean length3.0070133
Min length3

Characters and Unicode

Total characters301847
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 49917
41.9%
1.0 25518
21.4%
3.0 21131
17.7%
4.0 3111
 
2.6%
99.0 704
 
0.6%
(Missing) 18707
 
15.7%

Length

2023-10-05T00:45:02.314443image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:02.419479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 49917
49.7%
1.0 25518
25.4%
3.0 21131
21.1%
4.0 3111
 
3.1%
99.0 704
 
0.7%

Most occurring characters

ValueCountFrequency (%)
. 100381
33.3%
0 100381
33.3%
2 49917
16.5%
1 25518
 
8.5%
3 21131
 
7.0%
4 3111
 
1.0%
9 1408
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 201466
66.7%
Other Punctuation 100381
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 100381
49.8%
2 49917
24.8%
1 25518
 
12.7%
3 21131
 
10.5%
4 3111
 
1.5%
9 1408
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 100381
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301847
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 100381
33.3%
0 100381
33.3%
2 49917
16.5%
1 25518
 
8.5%
3 21131
 
7.0%
4 3111
 
1.0%
9 1408
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301847
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 100381
33.3%
0 100381
33.3%
2 49917
16.5%
1 25518
 
8.5%
3 21131
 
7.0%
4 3111
 
1.0%
9 1408
 
0.5%

W15
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing18707
Missing (%)15.7%
Memory size930.5 KiB
1.0
67228 
2.0
23081 
3.0
7094 
99.0
 
2763
4.0
 
215

Length

Max length4
Median length3
Mean length3.0275251
Min length3

Characters and Unicode

Total characters303906
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 67228
56.5%
2.0 23081
 
19.4%
3.0 7094
 
6.0%
99.0 2763
 
2.3%
4.0 215
 
0.2%
(Missing) 18707
 
15.7%

Length

2023-10-05T00:45:02.513495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:02.603520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 67228
67.0%
2.0 23081
 
23.0%
3.0 7094
 
7.1%
99.0 2763
 
2.8%
4.0 215
 
0.2%

Most occurring characters

ValueCountFrequency (%)
. 100381
33.0%
0 100381
33.0%
1 67228
22.1%
2 23081
 
7.6%
3 7094
 
2.3%
9 5526
 
1.8%
4 215
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 203525
67.0%
Other Punctuation 100381
33.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 100381
49.3%
1 67228
33.0%
2 23081
 
11.3%
3 7094
 
3.5%
9 5526
 
2.7%
4 215
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 100381
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 303906
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 100381
33.0%
0 100381
33.0%
1 67228
22.1%
2 23081
 
7.6%
3 7094
 
2.3%
9 5526
 
1.8%
4 215
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 303906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 100381
33.0%
0 100381
33.0%
1 67228
22.1%
2 23081
 
7.6%
3 7094
 
2.3%
9 5526
 
1.8%
4 215
 
0.1%

W15_1A
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing6517
Missing (%)5.5%
Memory size930.5 KiB
1.0
46916 
2.0
35336 
3.0
15513 
4.0
9667 
99.0
5139 

Length

Max length4
Median length3
Mean length3.0456512
Min length3

Characters and Unicode

Total characters342852
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row3.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 46916
39.4%
2.0 35336
29.7%
3.0 15513
 
13.0%
4.0 9667
 
8.1%
99.0 5139
 
4.3%
(Missing) 6517
 
5.5%

Length

2023-10-05T00:45:02.687545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:02.787550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 46916
41.7%
2.0 35336
31.4%
3.0 15513
 
13.8%
4.0 9667
 
8.6%
99.0 5139
 
4.6%

Most occurring characters

ValueCountFrequency (%)
. 112571
32.8%
0 112571
32.8%
1 46916
13.7%
2 35336
 
10.3%
3 15513
 
4.5%
9 10278
 
3.0%
4 9667
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 230281
67.2%
Other Punctuation 112571
32.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 112571
48.9%
1 46916
20.4%
2 35336
 
15.3%
3 15513
 
6.7%
9 10278
 
4.5%
4 9667
 
4.2%
Other Punctuation
ValueCountFrequency (%)
. 112571
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 342852
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 112571
32.8%
0 112571
32.8%
1 46916
13.7%
2 35336
 
10.3%
3 15513
 
4.5%
9 10278
 
3.0%
4 9667
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 342852
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 112571
32.8%
0 112571
32.8%
1 46916
13.7%
2 35336
 
10.3%
3 15513
 
4.5%
9 10278
 
3.0%
4 9667
 
2.8%

W15_1B
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing3502
Missing (%)2.9%
Memory size930.5 KiB
1.0
42866 
2.0
42348 
3.0
18549 
4.0
8262 
99.0
 
3561

Length

Max length4
Median length3
Mean length3.0308082
Min length3

Characters and Unicode

Total characters350319
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 42866
36.0%
2.0 42348
35.6%
3.0 18549
15.6%
4.0 8262
 
6.9%
99.0 3561
 
3.0%
(Missing) 3502
 
2.9%

Length

2023-10-05T00:45:02.885573image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:02.981594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 42866
37.1%
2.0 42348
36.6%
3.0 18549
16.0%
4.0 8262
 
7.1%
99.0 3561
 
3.1%

Most occurring characters

ValueCountFrequency (%)
. 115586
33.0%
0 115586
33.0%
1 42866
 
12.2%
2 42348
 
12.1%
3 18549
 
5.3%
4 8262
 
2.4%
9 7122
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 234733
67.0%
Other Punctuation 115586
33.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 115586
49.2%
1 42866
 
18.3%
2 42348
 
18.0%
3 18549
 
7.9%
4 8262
 
3.5%
9 7122
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 115586
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 350319
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 115586
33.0%
0 115586
33.0%
1 42866
 
12.2%
2 42348
 
12.1%
3 18549
 
5.3%
4 8262
 
2.4%
9 7122
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 350319
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 115586
33.0%
0 115586
33.0%
1 42866
 
12.2%
2 42348
 
12.1%
3 18549
 
5.3%
4 8262
 
2.4%
9 7122
 
2.0%

W15_1C
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing3502
Missing (%)2.9%
Memory size930.5 KiB
1.0
63019 
2.0
29471 
3.0
10456 
99.0
7201 
4.0
 
5439

Length

Max length4
Median length3
Mean length3.0622999
Min length3

Characters and Unicode

Total characters353959
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 63019
52.9%
2.0 29471
24.7%
3.0 10456
 
8.8%
99.0 7201
 
6.0%
4.0 5439
 
4.6%
(Missing) 3502
 
2.9%

Length

2023-10-05T00:45:03.074622image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:03.172637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 63019
54.5%
2.0 29471
25.5%
3.0 10456
 
9.0%
99.0 7201
 
6.2%
4.0 5439
 
4.7%

Most occurring characters

ValueCountFrequency (%)
. 115586
32.7%
0 115586
32.7%
1 63019
17.8%
2 29471
 
8.3%
9 14402
 
4.1%
3 10456
 
3.0%
4 5439
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 238373
67.3%
Other Punctuation 115586
32.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 115586
48.5%
1 63019
26.4%
2 29471
 
12.4%
9 14402
 
6.0%
3 10456
 
4.4%
4 5439
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 115586
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 353959
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 115586
32.7%
0 115586
32.7%
1 63019
17.8%
2 29471
 
8.3%
9 14402
 
4.1%
3 10456
 
3.0%
4 5439
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 353959
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 115586
32.7%
0 115586
32.7%
1 63019
17.8%
2 29471
 
8.3%
9 14402
 
4.1%
3 10456
 
3.0%
4 5439
 
1.5%

W15_1D
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing3502
Missing (%)2.9%
Memory size930.5 KiB
1.0
72208 
2.0
27812 
3.0
8620 
99.0
 
3632
4.0
 
3314

Length

Max length4
Median length3
Mean length3.0314225
Min length3

Characters and Unicode

Total characters350390
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 72208
60.6%
2.0 27812
 
23.4%
3.0 8620
 
7.2%
99.0 3632
 
3.0%
4.0 3314
 
2.8%
(Missing) 3502
 
2.9%

Length

2023-10-05T00:45:03.263669image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:03.355678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 72208
62.5%
2.0 27812
 
24.1%
3.0 8620
 
7.5%
99.0 3632
 
3.1%
4.0 3314
 
2.9%

Most occurring characters

ValueCountFrequency (%)
. 115586
33.0%
0 115586
33.0%
1 72208
20.6%
2 27812
 
7.9%
3 8620
 
2.5%
9 7264
 
2.1%
4 3314
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 234804
67.0%
Other Punctuation 115586
33.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 115586
49.2%
1 72208
30.8%
2 27812
 
11.8%
3 8620
 
3.7%
9 7264
 
3.1%
4 3314
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 115586
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 350390
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 115586
33.0%
0 115586
33.0%
1 72208
20.6%
2 27812
 
7.9%
3 8620
 
2.5%
9 7264
 
2.1%
4 3314
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 350390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 115586
33.0%
0 115586
33.0%
1 72208
20.6%
2 27812
 
7.9%
3 8620
 
2.5%
9 7264
 
2.1%
4 3314
 
0.9%

W15_1E
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing6517
Missing (%)5.5%
Memory size930.5 KiB
2.0
33362 
1.0
24482 
3.0
24036 
4.0
19859 
99.0
10832 

Length

Max length4
Median length3
Mean length3.0962237
Min length3

Characters and Unicode

Total characters348545
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row3.0
4th row99.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0 33362
28.0%
1.0 24482
20.6%
3.0 24036
20.2%
4.0 19859
16.7%
99.0 10832
 
9.1%
(Missing) 6517
 
5.5%

Length

2023-10-05T00:45:03.444697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:03.540732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 33362
29.6%
1.0 24482
21.7%
3.0 24036
21.4%
4.0 19859
17.6%
99.0 10832
 
9.6%

Most occurring characters

ValueCountFrequency (%)
. 112571
32.3%
0 112571
32.3%
2 33362
 
9.6%
1 24482
 
7.0%
3 24036
 
6.9%
9 21664
 
6.2%
4 19859
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 235974
67.7%
Other Punctuation 112571
32.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 112571
47.7%
2 33362
 
14.1%
1 24482
 
10.4%
3 24036
 
10.2%
9 21664
 
9.2%
4 19859
 
8.4%
Other Punctuation
ValueCountFrequency (%)
. 112571
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 348545
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 112571
32.3%
0 112571
32.3%
2 33362
 
9.6%
1 24482
 
7.0%
3 24036
 
6.9%
9 21664
 
6.2%
4 19859
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 348545
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 112571
32.3%
0 112571
32.3%
2 33362
 
9.6%
1 24482
 
7.0%
3 24036
 
6.9%
9 21664
 
6.2%
4 19859
 
5.7%

W15_2A
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing6517
Missing (%)5.5%
Memory size930.5 KiB
1.0
37160 
2.0
35986 
4.0
20859 
3.0
15424 
99.0
 
3142

Length

Max length4
Median length3
Mean length3.0279113
Min length3

Characters and Unicode

Total characters340855
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 37160
31.2%
2.0 35986
30.2%
4.0 20859
17.5%
3.0 15424
13.0%
99.0 3142
 
2.6%
(Missing) 6517
 
5.5%

Length

2023-10-05T00:45:03.632758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:04.442935image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 37160
33.0%
2.0 35986
32.0%
4.0 20859
18.5%
3.0 15424
13.7%
99.0 3142
 
2.8%

Most occurring characters

ValueCountFrequency (%)
. 112571
33.0%
0 112571
33.0%
1 37160
 
10.9%
2 35986
 
10.6%
4 20859
 
6.1%
3 15424
 
4.5%
9 6284
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 228284
67.0%
Other Punctuation 112571
33.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 112571
49.3%
1 37160
 
16.3%
2 35986
 
15.8%
4 20859
 
9.1%
3 15424
 
6.8%
9 6284
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 112571
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 340855
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 112571
33.0%
0 112571
33.0%
1 37160
 
10.9%
2 35986
 
10.6%
4 20859
 
6.1%
3 15424
 
4.5%
9 6284
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 340855
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 112571
33.0%
0 112571
33.0%
1 37160
 
10.9%
2 35986
 
10.6%
4 20859
 
6.1%
3 15424
 
4.5%
9 6284
 
1.8%

W15_2B
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing6517
Missing (%)5.5%
Memory size930.5 KiB
1.0
51364 
2.0
29899 
4.0
15176 
3.0
13256 
99.0
 
2876

Length

Max length4
Median length3
Mean length3.0255483
Min length3

Characters and Unicode

Total characters340589
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
1.0 51364
43.1%
2.0 29899
25.1%
4.0 15176
 
12.7%
3.0 13256
 
11.1%
99.0 2876
 
2.4%
(Missing) 6517
 
5.5%

Length

2023-10-05T00:45:04.528942image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:04.619963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 51364
45.6%
2.0 29899
26.6%
4.0 15176
 
13.5%
3.0 13256
 
11.8%
99.0 2876
 
2.6%

Most occurring characters

ValueCountFrequency (%)
. 112571
33.1%
0 112571
33.1%
1 51364
15.1%
2 29899
 
8.8%
4 15176
 
4.5%
3 13256
 
3.9%
9 5752
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 228018
66.9%
Other Punctuation 112571
33.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 112571
49.4%
1 51364
22.5%
2 29899
 
13.1%
4 15176
 
6.7%
3 13256
 
5.8%
9 5752
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 112571
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 340589
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 112571
33.1%
0 112571
33.1%
1 51364
15.1%
2 29899
 
8.8%
4 15176
 
4.5%
3 13256
 
3.9%
9 5752
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 340589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 112571
33.1%
0 112571
33.1%
1 51364
15.1%
2 29899
 
8.8%
4 15176
 
4.5%
3 13256
 
3.9%
9 5752
 
1.7%

MH1
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
2
60003 
1
53180 
3
 
4240
99
 
1665

Length

Max length2
Median length1
Mean length1.0139813
Min length1

Characters and Unicode

Total characters120753
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 60003
50.4%
1 53180
44.7%
3 4240
 
3.6%
99 1665
 
1.4%

Length

2023-10-05T00:45:04.706984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:04.796010image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2 60003
50.4%
1 53180
44.7%
3 4240
 
3.6%
99 1665
 
1.4%

Most occurring characters

ValueCountFrequency (%)
2 60003
49.7%
1 53180
44.0%
3 4240
 
3.5%
9 3330
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120753
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 60003
49.7%
1 53180
44.0%
3 4240
 
3.5%
9 3330
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 120753
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 60003
49.7%
1 53180
44.0%
3 4240
 
3.5%
9 3330
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120753
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 60003
49.7%
1 53180
44.0%
3 4240
 
3.5%
9 3330
 
2.8%

MH3A
Categorical

Distinct5
Distinct (%)< 0.1%
Missing1000
Missing (%)0.8%
Memory size930.5 KiB
1.0
62121 
2.0
37160 
3.0
10912 
4.0
 
4321
99.0
 
3574

Length

Max length4
Median length3
Mean length3.0302656
Min length3

Characters and Unicode

Total characters357838
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 62121
52.2%
2.0 37160
31.2%
3.0 10912
 
9.2%
4.0 4321
 
3.6%
99.0 3574
 
3.0%
(Missing) 1000
 
0.8%

Length

2023-10-05T00:45:04.879028image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:04.968043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 62121
52.6%
2.0 37160
31.5%
3.0 10912
 
9.2%
4.0 4321
 
3.7%
99.0 3574
 
3.0%

Most occurring characters

ValueCountFrequency (%)
. 118088
33.0%
0 118088
33.0%
1 62121
17.4%
2 37160
 
10.4%
3 10912
 
3.0%
9 7148
 
2.0%
4 4321
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 239750
67.0%
Other Punctuation 118088
33.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118088
49.3%
1 62121
25.9%
2 37160
 
15.5%
3 10912
 
4.6%
9 7148
 
3.0%
4 4321
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 118088
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 357838
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 118088
33.0%
0 118088
33.0%
1 62121
17.4%
2 37160
 
10.4%
3 10912
 
3.0%
9 7148
 
2.0%
4 4321
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 357838
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 118088
33.0%
0 118088
33.0%
1 62121
17.4%
2 37160
 
10.4%
3 10912
 
3.0%
9 7148
 
2.0%
4 4321
 
1.2%

MH3B
Categorical

Distinct5
Distinct (%)< 0.1%
Missing1000
Missing (%)0.8%
Memory size930.5 KiB
2.0
47472 
1.0
38088 
3.0
19183 
4.0
7973 
99.0
5372 

Length

Max length4
Median length3
Mean length3.0454915
Min length3

Characters and Unicode

Total characters359636
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row2.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 47472
39.9%
1.0 38088
32.0%
3.0 19183
16.1%
4.0 7973
 
6.7%
99.0 5372
 
4.5%
(Missing) 1000
 
0.8%

Length

2023-10-05T00:45:05.056073image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:05.152095image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 47472
40.2%
1.0 38088
32.3%
3.0 19183
16.2%
4.0 7973
 
6.8%
99.0 5372
 
4.5%

Most occurring characters

ValueCountFrequency (%)
. 118088
32.8%
0 118088
32.8%
2 47472
13.2%
1 38088
 
10.6%
3 19183
 
5.3%
9 10744
 
3.0%
4 7973
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 241548
67.2%
Other Punctuation 118088
32.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118088
48.9%
2 47472
19.7%
1 38088
 
15.8%
3 19183
 
7.9%
9 10744
 
4.4%
4 7973
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 118088
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 359636
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 118088
32.8%
0 118088
32.8%
2 47472
13.2%
1 38088
 
10.6%
3 19183
 
5.3%
9 10744
 
3.0%
4 7973
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 359636
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 118088
32.8%
0 118088
32.8%
2 47472
13.2%
1 38088
 
10.6%
3 19183
 
5.3%
9 10744
 
3.0%
4 7973
 
2.2%

MH3C
Categorical

Distinct5
Distinct (%)< 0.1%
Missing1000
Missing (%)0.8%
Memory size930.5 KiB
1.0
65583 
2.0
31694 
3.0
9623 
99.0
 
6210
4.0
 
4978

Length

Max length4
Median length3
Mean length3.0525879
Min length3

Characters and Unicode

Total characters360474
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 65583
55.1%
2.0 31694
26.6%
3.0 9623
 
8.1%
99.0 6210
 
5.2%
4.0 4978
 
4.2%
(Missing) 1000
 
0.8%

Length

2023-10-05T00:45:05.245104image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:05.336125image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 65583
55.5%
2.0 31694
26.8%
3.0 9623
 
8.1%
99.0 6210
 
5.3%
4.0 4978
 
4.2%

Most occurring characters

ValueCountFrequency (%)
. 118088
32.8%
0 118088
32.8%
1 65583
18.2%
2 31694
 
8.8%
9 12420
 
3.4%
3 9623
 
2.7%
4 4978
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 242386
67.2%
Other Punctuation 118088
32.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118088
48.7%
1 65583
27.1%
2 31694
 
13.1%
9 12420
 
5.1%
3 9623
 
4.0%
4 4978
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 118088
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 360474
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 118088
32.8%
0 118088
32.8%
1 65583
18.2%
2 31694
 
8.8%
9 12420
 
3.4%
3 9623
 
2.7%
4 4978
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360474
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 118088
32.8%
0 118088
32.8%
1 65583
18.2%
2 31694
 
8.8%
9 12420
 
3.4%
3 9623
 
2.7%
4 4978
 
1.4%

MH3D
Categorical

Distinct5
Distinct (%)< 0.1%
Missing1000
Missing (%)0.8%
Memory size930.5 KiB
2.0
42792 
1.0
38280 
3.0
20690 
4.0
10695 
99.0
5631 

Length

Max length4
Median length3
Mean length3.0476848
Min length3

Characters and Unicode

Total characters359895
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row2.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 42792
35.9%
1.0 38280
32.1%
3.0 20690
17.4%
4.0 10695
 
9.0%
99.0 5631
 
4.7%
(Missing) 1000
 
0.8%

Length

2023-10-05T00:45:05.432146image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:05.528168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 42792
36.2%
1.0 38280
32.4%
3.0 20690
17.5%
4.0 10695
 
9.1%
99.0 5631
 
4.8%

Most occurring characters

ValueCountFrequency (%)
. 118088
32.8%
0 118088
32.8%
2 42792
 
11.9%
1 38280
 
10.6%
3 20690
 
5.7%
9 11262
 
3.1%
4 10695
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 241807
67.2%
Other Punctuation 118088
32.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118088
48.8%
2 42792
 
17.7%
1 38280
 
15.8%
3 20690
 
8.6%
9 11262
 
4.7%
4 10695
 
4.4%
Other Punctuation
ValueCountFrequency (%)
. 118088
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 359895
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 118088
32.8%
0 118088
32.8%
2 42792
 
11.9%
1 38280
 
10.6%
3 20690
 
5.7%
9 11262
 
3.1%
4 10695
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 359895
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 118088
32.8%
0 118088
32.8%
2 42792
 
11.9%
1 38280
 
10.6%
3 20690
 
5.7%
9 11262
 
3.1%
4 10695
 
3.0%

MH4A
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing2013
Missing (%)1.7%
Memory size930.5 KiB
1.0
89796 
2.0
20332 
3.0
 
2972
99.0
 
2143
4.0
 
1832

Length

Max length4
Median length3
Mean length3.0183045
Min length3

Characters and Unicode

Total characters353368
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 89796
75.4%
2.0 20332
 
17.1%
3.0 2972
 
2.5%
99.0 2143
 
1.8%
4.0 1832
 
1.5%
(Missing) 2013
 
1.7%

Length

2023-10-05T00:45:05.621188image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:05.716211image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 89796
76.7%
2.0 20332
 
17.4%
3.0 2972
 
2.5%
99.0 2143
 
1.8%
4.0 1832
 
1.6%

Most occurring characters

ValueCountFrequency (%)
. 117075
33.1%
0 117075
33.1%
1 89796
25.4%
2 20332
 
5.8%
9 4286
 
1.2%
3 2972
 
0.8%
4 1832
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 236293
66.9%
Other Punctuation 117075
33.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 117075
49.5%
1 89796
38.0%
2 20332
 
8.6%
9 4286
 
1.8%
3 2972
 
1.3%
4 1832
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 117075
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 353368
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 117075
33.1%
0 117075
33.1%
1 89796
25.4%
2 20332
 
5.8%
9 4286
 
1.2%
3 2972
 
0.8%
4 1832
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 353368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 117075
33.1%
0 117075
33.1%
1 89796
25.4%
2 20332
 
5.8%
9 4286
 
1.2%
3 2972
 
0.8%
4 1832
 
0.5%

MH4B
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing2013
Missing (%)1.7%
Memory size930.5 KiB
1.0
66898 
2.0
34639 
3.0
8198 
4.0
 
3963
99.0
 
3377

Length

Max length4
Median length3
Mean length3.0288448
Min length3

Characters and Unicode

Total characters354602
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 66898
56.2%
2.0 34639
29.1%
3.0 8198
 
6.9%
4.0 3963
 
3.3%
99.0 3377
 
2.8%
(Missing) 2013
 
1.7%

Length

2023-10-05T00:45:05.804230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:05.900252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 66898
57.1%
2.0 34639
29.6%
3.0 8198
 
7.0%
4.0 3963
 
3.4%
99.0 3377
 
2.9%

Most occurring characters

ValueCountFrequency (%)
. 117075
33.0%
0 117075
33.0%
1 66898
18.9%
2 34639
 
9.8%
3 8198
 
2.3%
9 6754
 
1.9%
4 3963
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 237527
67.0%
Other Punctuation 117075
33.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 117075
49.3%
1 66898
28.2%
2 34639
 
14.6%
3 8198
 
3.5%
9 6754
 
2.8%
4 3963
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 117075
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 354602
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 117075
33.0%
0 117075
33.0%
1 66898
18.9%
2 34639
 
9.8%
3 8198
 
2.3%
9 6754
 
1.9%
4 3963
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 354602
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 117075
33.0%
0 117075
33.0%
1 66898
18.9%
2 34639
 
9.8%
3 8198
 
2.3%
9 6754
 
1.9%
4 3963
 
1.1%

MH5
Categorical

Distinct4
Distinct (%)< 0.1%
Missing1000
Missing (%)0.8%
Memory size930.5 KiB
2.0
47703 
3.0
35815 
1.0
26919 
99.0
7651 

Length

Max length4
Median length3
Mean length3.0647907
Min length3

Characters and Unicode

Total characters361915
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 47703
40.1%
3.0 35815
30.1%
1.0 26919
22.6%
99.0 7651
 
6.4%
(Missing) 1000
 
0.8%

Length

2023-10-05T00:45:05.985289image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:06.078292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 47703
40.4%
3.0 35815
30.3%
1.0 26919
22.8%
99.0 7651
 
6.5%

Most occurring characters

ValueCountFrequency (%)
. 118088
32.6%
0 118088
32.6%
2 47703
13.2%
3 35815
 
9.9%
1 26919
 
7.4%
9 15302
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 243827
67.4%
Other Punctuation 118088
32.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118088
48.4%
2 47703
19.6%
3 35815
 
14.7%
1 26919
 
11.0%
9 15302
 
6.3%
Other Punctuation
ValueCountFrequency (%)
. 118088
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 361915
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 118088
32.6%
0 118088
32.6%
2 47703
13.2%
3 35815
 
9.9%
1 26919
 
7.4%
9 15302
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 361915
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 118088
32.6%
0 118088
32.6%
2 47703
13.2%
3 35815
 
9.9%
1 26919
 
7.4%
9 15302
 
4.2%

MH6
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
2
69635 
1
46197 
99
 
3256

Length

Max length2
Median length1
Mean length1.0273411
Min length1

Characters and Unicode

Total characters122344
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 69635
58.5%
1 46197
38.8%
99 3256
 
2.7%

Length

2023-10-05T00:45:06.173314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:06.263335image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2 69635
58.5%
1 46197
38.8%
99 3256
 
2.7%

Most occurring characters

ValueCountFrequency (%)
2 69635
56.9%
1 46197
37.8%
9 6512
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 122344
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 69635
56.9%
1 46197
37.8%
9 6512
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 122344
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 69635
56.9%
1 46197
37.8%
9 6512
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 69635
56.9%
1 46197
37.8%
9 6512
 
5.3%

MH7A
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing1000
Missing (%)0.8%
Memory size930.5 KiB
2.0
92839 
1.0
23971 
99.0
 
1278

Length

Max length4
Median length3
Mean length3.0108224
Min length3

Characters and Unicode

Total characters355542
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 92839
78.0%
1.0 23971
 
20.1%
99.0 1278
 
1.1%
(Missing) 1000
 
0.8%

Length

2023-10-05T00:45:06.346365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:06.429384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 92839
78.6%
1.0 23971
 
20.3%
99.0 1278
 
1.1%

Most occurring characters

ValueCountFrequency (%)
. 118088
33.2%
0 118088
33.2%
2 92839
26.1%
1 23971
 
6.7%
9 2556
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 237454
66.8%
Other Punctuation 118088
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118088
49.7%
2 92839
39.1%
1 23971
 
10.1%
9 2556
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 118088
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 355542
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 118088
33.2%
0 118088
33.2%
2 92839
26.1%
1 23971
 
6.7%
9 2556
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 355542
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 118088
33.2%
0 118088
33.2%
2 92839
26.1%
1 23971
 
6.7%
9 2556
 
0.7%

MH7B
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct89
Distinct (%)0.4%
Missing95117
Missing (%)79.9%
Infinite0
Infinite (%)0.0%
Mean30.598473
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.5 KiB
2023-10-05T00:45:06.518390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q118
median25
Q336
95-th percentile75
Maximum99
Range98
Interquartile range (IQR)18

Descriptive statistics

Standard deviation19.674473
Coefficient of variation (CV)0.6429887
Kurtosis4.4935897
Mean30.598473
Median Absolute Deviation (MAD)8
Skewness2.073997
Sum733476
Variance387.08487
MonotonicityNot monotonic
2023-10-05T00:45:06.624415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 1529
 
1.3%
18 1349
 
1.1%
25 1339
 
1.1%
15 1196
 
1.0%
30 1184
 
1.0%
99 1131
 
0.9%
17 998
 
0.8%
16 968
 
0.8%
19 863
 
0.7%
22 817
 
0.7%
Other values (79) 12597
 
10.6%
(Missing) 95117
79.9%
ValueCountFrequency (%)
1 12
 
< 0.1%
2 16
 
< 0.1%
3 15
 
< 0.1%
4 11
 
< 0.1%
5 50
 
< 0.1%
6 60
 
0.1%
7 46
 
< 0.1%
8 84
0.1%
9 71
 
0.1%
10 208
0.2%
ValueCountFrequency (%)
99 1131
0.9%
97 4
 
< 0.1%
88 2
 
< 0.1%
87 1
 
< 0.1%
85 3
 
< 0.1%
84 2
 
< 0.1%
83 2
 
< 0.1%
82 2
 
< 0.1%
81 2
 
< 0.1%
80 17
 
< 0.1%

MH7B_2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.5%
Missing117961
Missing (%)99.1%
Infinite0
Infinite (%)0.0%
Mean29.156167
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.5 KiB
2023-10-05T00:45:06.720438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q399
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)97

Descriptive statistics

Standard deviation42.766337
Coefficient of variation (CV)1.4668025
Kurtosis-0.95452792
Mean29.156167
Median Absolute Deviation (MAD)1
Skewness1.0219671
Sum32859
Variance1828.9596
MonotonicityNot monotonic
2023-10-05T00:45:06.799456image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
99 307
 
0.3%
3 274
 
0.2%
2 225
 
0.2%
4 127
 
0.1%
5 123
 
0.1%
1 71
 
0.1%
(Missing) 117961
99.1%
ValueCountFrequency (%)
1 71
 
0.1%
2 225
0.2%
3 274
0.2%
4 127
0.1%
5 123
0.1%
99 307
0.3%
ValueCountFrequency (%)
99 307
0.3%
5 123
0.1%
4 127
0.1%
3 274
0.2%
2 225
0.2%
1 71
 
0.1%

MH7C
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing95117
Missing (%)79.9%
Memory size930.5 KiB
1.0
17445 
2.0
6315 
99.0
 
211

Length

Max length4
Median length3
Mean length3.0088023
Min length3

Characters and Unicode

Total characters72124
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 17445
 
14.6%
2.0 6315
 
5.3%
99.0 211
 
0.2%
(Missing) 95117
79.9%

Length

2023-10-05T00:45:06.886475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:06.975494image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 17445
72.8%
2.0 6315
 
26.3%
99.0 211
 
0.9%

Most occurring characters

ValueCountFrequency (%)
. 23971
33.2%
0 23971
33.2%
1 17445
24.2%
2 6315
 
8.8%
9 422
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48153
66.8%
Other Punctuation 23971
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23971
49.8%
1 17445
36.2%
2 6315
 
13.1%
9 422
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 23971
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72124
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 23971
33.2%
0 23971
33.2%
1 17445
24.2%
2 6315
 
8.8%
9 422
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 23971
33.2%
0 23971
33.2%
1 17445
24.2%
2 6315
 
8.8%
9 422
 
0.6%

MH8A
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing95117
Missing (%)79.9%
Memory size930.5 KiB
2.0
13581 
1.0
10285 
99.0
 
105

Length

Max length4
Median length3
Mean length3.0043803
Min length3

Characters and Unicode

Total characters72018
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 13581
 
11.4%
1.0 10285
 
8.6%
99.0 105
 
0.1%
(Missing) 95117
79.9%

Length

2023-10-05T00:45:07.052511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:07.133544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 13581
56.7%
1.0 10285
42.9%
99.0 105
 
0.4%

Most occurring characters

ValueCountFrequency (%)
. 23971
33.3%
0 23971
33.3%
2 13581
18.9%
1 10285
14.3%
9 210
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48047
66.7%
Other Punctuation 23971
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23971
49.9%
2 13581
28.3%
1 10285
21.4%
9 210
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 23971
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72018
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 23971
33.3%
0 23971
33.3%
2 13581
18.9%
1 10285
14.3%
9 210
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72018
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 23971
33.3%
0 23971
33.3%
2 13581
18.9%
1 10285
14.3%
9 210
 
0.3%

MH8B
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing95412
Missing (%)80.1%
Memory size930.5 KiB
2.0
13303 
1.0
10246 
99.0
 
127

Length

Max length4
Median length3
Mean length3.0053641
Min length3

Characters and Unicode

Total characters71155
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 13303
 
11.2%
1.0 10246
 
8.6%
99.0 127
 
0.1%
(Missing) 95412
80.1%

Length

2023-10-05T00:45:07.221550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:07.310583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 13303
56.2%
1.0 10246
43.3%
99.0 127
 
0.5%

Most occurring characters

ValueCountFrequency (%)
. 23676
33.3%
0 23676
33.3%
2 13303
18.7%
1 10246
14.4%
9 254
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47479
66.7%
Other Punctuation 23676
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23676
49.9%
2 13303
28.0%
1 10246
21.6%
9 254
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 23676
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 71155
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 23676
33.3%
0 23676
33.3%
2 13303
18.7%
1 10246
14.4%
9 254
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 23676
33.3%
0 23676
33.3%
2 13303
18.7%
1 10246
14.4%
9 254
 
0.4%

MH8C
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing95117
Missing (%)79.9%
Memory size930.5 KiB
1.0
19104 
2.0
4789 
99.0
 
78

Length

Max length4
Median length3
Mean length3.0032539
Min length3

Characters and Unicode

Total characters71991
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 19104
 
16.0%
2.0 4789
 
4.0%
99.0 78
 
0.1%
(Missing) 95117
79.9%

Length

2023-10-05T00:45:07.387588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:07.470607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 19104
79.7%
2.0 4789
 
20.0%
99.0 78
 
0.3%

Most occurring characters

ValueCountFrequency (%)
. 23971
33.3%
0 23971
33.3%
1 19104
26.5%
2 4789
 
6.7%
9 156
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48020
66.7%
Other Punctuation 23971
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23971
49.9%
1 19104
39.8%
2 4789
 
10.0%
9 156
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 23971
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 71991
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 23971
33.3%
0 23971
33.3%
1 19104
26.5%
2 4789
 
6.7%
9 156
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 23971
33.3%
0 23971
33.3%
1 19104
26.5%
2 4789
 
6.7%
9 156
 
0.2%

MH8D
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing95117
Missing (%)79.9%
Memory size930.5 KiB
2.0
12691 
1.0
11191 
99.0
 
89

Length

Max length4
Median length3
Mean length3.0037128
Min length3

Characters and Unicode

Total characters72002
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 12691
 
10.7%
1.0 11191
 
9.4%
99.0 89
 
0.1%
(Missing) 95117
79.9%

Length

2023-10-05T00:45:07.555626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:07.652647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 12691
52.9%
1.0 11191
46.7%
99.0 89
 
0.4%

Most occurring characters

ValueCountFrequency (%)
. 23971
33.3%
0 23971
33.3%
2 12691
17.6%
1 11191
15.5%
9 178
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48031
66.7%
Other Punctuation 23971
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23971
49.9%
2 12691
26.4%
1 11191
23.3%
9 178
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 23971
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72002
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 23971
33.3%
0 23971
33.3%
2 12691
17.6%
1 11191
15.5%
9 178
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 23971
33.3%
0 23971
33.3%
2 12691
17.6%
1 11191
15.5%
9 178
 
0.2%

MH8E
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing95117
Missing (%)79.9%
Memory size930.5 KiB
1.0
17430 
2.0
6393 
99.0
 
148

Length

Max length4
Median length3
Mean length3.0061741
Min length3

Characters and Unicode

Total characters72061
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 17430
 
14.6%
2.0 6393
 
5.4%
99.0 148
 
0.1%
(Missing) 95117
79.9%

Length

2023-10-05T00:45:07.749669image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:07.834694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 17430
72.7%
2.0 6393
 
26.7%
99.0 148
 
0.6%

Most occurring characters

ValueCountFrequency (%)
. 23971
33.3%
0 23971
33.3%
1 17430
24.2%
2 6393
 
8.9%
9 296
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48090
66.7%
Other Punctuation 23971
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23971
49.8%
1 17430
36.2%
2 6393
 
13.3%
9 296
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 23971
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72061
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 23971
33.3%
0 23971
33.3%
1 17430
24.2%
2 6393
 
8.9%
9 296
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 23971
33.3%
0 23971
33.3%
1 17430
24.2%
2 6393
 
8.9%
9 296
 
0.4%

MH8F
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing95117
Missing (%)79.9%
Memory size930.5 KiB
1.0
12252 
2.0
11456 
99.0
 
263

Length

Max length4
Median length3
Mean length3.0109716
Min length3

Characters and Unicode

Total characters72176
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row99.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 12252
 
10.3%
2.0 11456
 
9.6%
99.0 263
 
0.2%
(Missing) 95117
79.9%

Length

2023-10-05T00:45:07.912706image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:07.998725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 12252
51.1%
2.0 11456
47.8%
99.0 263
 
1.1%

Most occurring characters

ValueCountFrequency (%)
. 23971
33.2%
0 23971
33.2%
1 12252
17.0%
2 11456
15.9%
9 526
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48205
66.8%
Other Punctuation 23971
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23971
49.7%
1 12252
25.4%
2 11456
23.8%
9 526
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 23971
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 23971
33.2%
0 23971
33.2%
1 12252
17.0%
2 11456
15.9%
9 526
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 23971
33.2%
0 23971
33.2%
1 12252
17.0%
2 11456
15.9%
9 526
 
0.7%

MH8G
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing95117
Missing (%)79.9%
Memory size930.5 KiB
1.0
14970 
2.0
8730 
99.0
 
271

Length

Max length4
Median length3
Mean length3.0113053
Min length3

Characters and Unicode

Total characters72184
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 14970
 
12.6%
2.0 8730
 
7.3%
99.0 271
 
0.2%
(Missing) 95117
79.9%

Length

2023-10-05T00:45:08.079744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:08.178767image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 14970
62.5%
2.0 8730
36.4%
99.0 271
 
1.1%

Most occurring characters

ValueCountFrequency (%)
. 23971
33.2%
0 23971
33.2%
1 14970
20.7%
2 8730
 
12.1%
9 542
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48213
66.8%
Other Punctuation 23971
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23971
49.7%
1 14970
31.0%
2 8730
 
18.1%
9 542
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 23971
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72184
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 23971
33.2%
0 23971
33.2%
1 14970
20.7%
2 8730
 
12.1%
9 542
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72184
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 23971
33.2%
0 23971
33.2%
1 14970
20.7%
2 8730
 
12.1%
9 542
 
0.8%

MH8H
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing95117
Missing (%)79.9%
Memory size930.5 KiB
1.0
17653 
2.0
6204 
99.0
 
114

Length

Max length4
Median length3
Mean length3.0047557
Min length3

Characters and Unicode

Total characters72027
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 17653
 
14.8%
2.0 6204
 
5.2%
99.0 114
 
0.1%
(Missing) 95117
79.9%

Length

2023-10-05T00:45:08.268788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:08.357807image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 17653
73.6%
2.0 6204
 
25.9%
99.0 114
 
0.5%

Most occurring characters

ValueCountFrequency (%)
. 23971
33.3%
0 23971
33.3%
1 17653
24.5%
2 6204
 
8.6%
9 228
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48056
66.7%
Other Punctuation 23971
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23971
49.9%
1 17653
36.7%
2 6204
 
12.9%
9 228
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 23971
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72027
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 23971
33.3%
0 23971
33.3%
1 17653
24.5%
2 6204
 
8.6%
9 228
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72027
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 23971
33.3%
0 23971
33.3%
1 17653
24.5%
2 6204
 
8.6%
9 228
 
0.3%

MH9A
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing108834
Missing (%)91.4%
Memory size930.5 KiB
1.0
6456 
2.0
2933 
3.0
748 
99.0
 
117

Length

Max length4
Median length3
Mean length3.0114102
Min length3

Characters and Unicode

Total characters30879
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 6456
 
5.4%
2.0 2933
 
2.5%
3.0 748
 
0.6%
99.0 117
 
0.1%
(Missing) 108834
91.4%

Length

2023-10-05T00:45:08.444826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:08.538848image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 6456
63.0%
2.0 2933
28.6%
3.0 748
 
7.3%
99.0 117
 
1.1%

Most occurring characters

ValueCountFrequency (%)
. 10254
33.2%
0 10254
33.2%
1 6456
20.9%
2 2933
 
9.5%
3 748
 
2.4%
9 234
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20625
66.8%
Other Punctuation 10254
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10254
49.7%
1 6456
31.3%
2 2933
 
14.2%
3 748
 
3.6%
9 234
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 10254
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30879
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 10254
33.2%
0 10254
33.2%
1 6456
20.9%
2 2933
 
9.5%
3 748
 
2.4%
9 234
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30879
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 10254
33.2%
0 10254
33.2%
1 6456
20.9%
2 2933
 
9.5%
3 748
 
2.4%
9 234
 
0.8%

MH9B
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing108906
Missing (%)91.5%
Memory size930.5 KiB
1.0
6776 
2.0
2789 
3.0
 
532
99.0
 
85

Length

Max length4
Median length3
Mean length3.0083481
Min length3

Characters and Unicode

Total characters30631
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 6776
 
5.7%
2.0 2789
 
2.3%
3.0 532
 
0.4%
99.0 85
 
0.1%
(Missing) 108906
91.5%

Length

2023-10-05T00:45:08.625873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:08.708903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 6776
66.5%
2.0 2789
27.4%
3.0 532
 
5.2%
99.0 85
 
0.8%

Most occurring characters

ValueCountFrequency (%)
. 10182
33.2%
0 10182
33.2%
1 6776
22.1%
2 2789
 
9.1%
3 532
 
1.7%
9 170
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20449
66.8%
Other Punctuation 10182
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10182
49.8%
1 6776
33.1%
2 2789
 
13.6%
3 532
 
2.6%
9 170
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 10182
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30631
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 10182
33.2%
0 10182
33.2%
1 6776
22.1%
2 2789
 
9.1%
3 532
 
1.7%
9 170
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30631
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 10182
33.2%
0 10182
33.2%
1 6776
22.1%
2 2789
 
9.1%
3 532
 
1.7%
9 170
 
0.6%

MH9C
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing100289
Missing (%)84.2%
Memory size930.5 KiB
1.0
12395 
2.0
5478 
3.0
 
831
99.0
 
95

Length

Max length4
Median length3
Mean length3.0050535
Min length3

Characters and Unicode

Total characters56492
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 12395
 
10.4%
2.0 5478
 
4.6%
3.0 831
 
0.7%
99.0 95
 
0.1%
(Missing) 100289
84.2%

Length

2023-10-05T00:45:08.798906image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:08.890927image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 12395
65.9%
2.0 5478
29.1%
3.0 831
 
4.4%
99.0 95
 
0.5%

Most occurring characters

ValueCountFrequency (%)
. 18799
33.3%
0 18799
33.3%
1 12395
21.9%
2 5478
 
9.7%
3 831
 
1.5%
9 190
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37693
66.7%
Other Punctuation 18799
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18799
49.9%
1 12395
32.9%
2 5478
 
14.5%
3 831
 
2.2%
9 190
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 18799
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 56492
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 18799
33.3%
0 18799
33.3%
1 12395
21.9%
2 5478
 
9.7%
3 831
 
1.5%
9 190
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 18799
33.3%
0 18799
33.3%
1 12395
21.9%
2 5478
 
9.7%
3 831
 
1.5%
9 190
 
0.3%

MH9D
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing107957
Missing (%)90.7%
Memory size930.5 KiB
1.0
6254 
2.0
3703 
3.0
1064 
99.0
 
110

Length

Max length4
Median length3
Mean length3.0098823
Min length3

Characters and Unicode

Total characters33503
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 6254
 
5.3%
2.0 3703
 
3.1%
3.0 1064
 
0.9%
99.0 110
 
0.1%
(Missing) 107957
90.7%

Length

2023-10-05T00:45:08.983949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:09.065984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 6254
56.2%
2.0 3703
33.3%
3.0 1064
 
9.6%
99.0 110
 
1.0%

Most occurring characters

ValueCountFrequency (%)
. 11131
33.2%
0 11131
33.2%
1 6254
18.7%
2 3703
 
11.1%
3 1064
 
3.2%
9 220
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22372
66.8%
Other Punctuation 11131
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11131
49.8%
1 6254
28.0%
2 3703
 
16.6%
3 1064
 
4.8%
9 220
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 11131
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33503
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 11131
33.2%
0 11131
33.2%
1 6254
18.7%
2 3703
 
11.1%
3 1064
 
3.2%
9 220
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33503
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 11131
33.2%
0 11131
33.2%
1 6254
18.7%
2 3703
 
11.1%
3 1064
 
3.2%
9 220
 
0.7%

MH9E
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing101787
Missing (%)85.5%
Memory size930.5 KiB
1.0
12278 
2.0
4360 
3.0
 
561
99.0
 
102

Length

Max length4
Median length3
Mean length3.0058956
Min length3

Characters and Unicode

Total characters52005
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 12278
 
10.3%
2.0 4360
 
3.7%
3.0 561
 
0.5%
99.0 102
 
0.1%
(Missing) 101787
85.5%

Length

2023-10-05T00:45:09.175998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:09.270025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 12278
71.0%
2.0 4360
 
25.2%
3.0 561
 
3.2%
99.0 102
 
0.6%

Most occurring characters

ValueCountFrequency (%)
. 17301
33.3%
0 17301
33.3%
1 12278
23.6%
2 4360
 
8.4%
3 561
 
1.1%
9 204
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34704
66.7%
Other Punctuation 17301
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17301
49.9%
1 12278
35.4%
2 4360
 
12.6%
3 561
 
1.6%
9 204
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 17301
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 52005
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 17301
33.3%
0 17301
33.3%
1 12278
23.6%
2 4360
 
8.4%
3 561
 
1.1%
9 204
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 17301
33.3%
0 17301
33.3%
1 12278
23.6%
2 4360
 
8.4%
3 561
 
1.1%
9 204
 
0.4%

MH9F
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing106877
Missing (%)89.7%
Memory size930.5 KiB
1.0
7320 
2.0
3734 
3.0
991 
99.0
 
166

Length

Max length4
Median length3
Mean length3.0135943
Min length3

Characters and Unicode

Total characters36799
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 7320
 
6.1%
2.0 3734
 
3.1%
3.0 991
 
0.8%
99.0 166
 
0.1%
(Missing) 106877
89.7%

Length

2023-10-05T00:45:09.358039image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:09.453055image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7320
59.9%
2.0 3734
30.6%
3.0 991
 
8.1%
99.0 166
 
1.4%

Most occurring characters

ValueCountFrequency (%)
. 12211
33.2%
0 12211
33.2%
1 7320
19.9%
2 3734
 
10.1%
3 991
 
2.7%
9 332
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24588
66.8%
Other Punctuation 12211
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12211
49.7%
1 7320
29.8%
2 3734
 
15.2%
3 991
 
4.0%
9 332
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 12211
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36799
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 12211
33.2%
0 12211
33.2%
1 7320
19.9%
2 3734
 
10.1%
3 991
 
2.7%
9 332
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36799
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 12211
33.2%
0 12211
33.2%
1 7320
19.9%
2 3734
 
10.1%
3 991
 
2.7%
9 332
 
0.9%

MH9G
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing104221
Missing (%)87.5%
Memory size930.5 KiB
1.0
9157 
2.0
4624 
3.0
918 
99.0
 
168

Length

Max length4
Median length3
Mean length3.0113002
Min length3

Characters and Unicode

Total characters44769
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 9157
 
7.7%
2.0 4624
 
3.9%
3.0 918
 
0.8%
99.0 168
 
0.1%
(Missing) 104221
87.5%

Length

2023-10-05T00:45:09.546075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:09.639102image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 9157
61.6%
2.0 4624
31.1%
3.0 918
 
6.2%
99.0 168
 
1.1%

Most occurring characters

ValueCountFrequency (%)
. 14867
33.2%
0 14867
33.2%
1 9157
20.5%
2 4624
 
10.3%
3 918
 
2.1%
9 336
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29902
66.8%
Other Punctuation 14867
33.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14867
49.7%
1 9157
30.6%
2 4624
 
15.5%
3 918
 
3.1%
9 336
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 14867
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44769
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 14867
33.2%
0 14867
33.2%
1 9157
20.5%
2 4624
 
10.3%
3 918
 
2.1%
9 336
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44769
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 14867
33.2%
0 14867
33.2%
1 9157
20.5%
2 4624
 
10.3%
3 918
 
2.1%
9 336
 
0.8%

MH9H
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing101604
Missing (%)85.3%
Memory size930.5 KiB
1.0
12730 
2.0
4159 
3.0
 
518
99.0
 
77

Length

Max length4
Median length3
Mean length3.004404
Min length3

Characters and Unicode

Total characters52529
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 12730
 
10.7%
2.0 4159
 
3.5%
3.0 518
 
0.4%
99.0 77
 
0.1%
(Missing) 101604
85.3%

Length

2023-10-05T00:45:09.725127image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:09.823137image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 12730
72.8%
2.0 4159
 
23.8%
3.0 518
 
3.0%
99.0 77
 
0.4%

Most occurring characters

ValueCountFrequency (%)
. 17484
33.3%
0 17484
33.3%
1 12730
24.2%
2 4159
 
7.9%
3 518
 
1.0%
9 154
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35045
66.7%
Other Punctuation 17484
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17484
49.9%
1 12730
36.3%
2 4159
 
11.9%
3 518
 
1.5%
9 154
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 17484
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 52529
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 17484
33.3%
0 17484
33.3%
1 12730
24.2%
2 4159
 
7.9%
3 518
 
1.0%
9 154
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52529
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 17484
33.3%
0 17484
33.3%
1 12730
24.2%
2 4159
 
7.9%
3 518
 
1.0%
9 154
 
0.3%

W27
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
1
97080 
2
21748 
99
 
260

Length

Max length2
Median length1
Mean length1.0021833
Min length1

Characters and Unicode

Total characters119348
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 97080
81.5%
2 21748
 
18.3%
99 260
 
0.2%

Length

2023-10-05T00:45:09.920160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:10.010181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1 97080
81.5%
2 21748
 
18.3%
99 260
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 97080
81.3%
2 21748
 
18.2%
9 520
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119348
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 97080
81.3%
2 21748
 
18.2%
9 520
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 119348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 97080
81.3%
2 21748
 
18.2%
9 520
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 97080
81.3%
2 21748
 
18.2%
9 520
 
0.4%

W28
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing22008
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean3.5769262
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.5 KiB
2023-10-05T00:45:10.101212image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum99
Range98
Interquartile range (IQR)2

Descriptive statistics

Standard deviation7.1901002
Coefficient of variation (CV)2.0101337
Kurtosis166.6987
Mean3.5769262
Median Absolute Deviation (MAD)1
Skewness12.780627
Sum347248
Variance51.69754
MonotonicityNot monotonic
2023-10-05T00:45:10.175230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 45347
38.1%
1 13828
 
11.6%
4 13141
 
11.0%
2 11381
 
9.6%
5 7628
 
6.4%
6 5224
 
4.4%
99 531
 
0.4%
(Missing) 22008
18.5%
ValueCountFrequency (%)
1 13828
 
11.6%
2 11381
 
9.6%
3 45347
38.1%
4 13141
 
11.0%
5 7628
 
6.4%
6 5224
 
4.4%
99 531
 
0.4%
ValueCountFrequency (%)
99 531
 
0.4%
6 5224
 
4.4%
5 7628
 
6.4%
4 13141
 
11.0%
3 45347
38.1%
2 11381
 
9.6%
1 13828
 
11.6%

W29
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing22008
Missing (%)18.5%
Memory size930.5 KiB
3.0
53966 
2.0
21205 
1.0
10850 
4.0
10366 
99.0
 
693

Length

Max length4
Median length3
Mean length3.0071384
Min length3

Characters and Unicode

Total characters291933
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 53966
45.3%
2.0 21205
 
17.8%
1.0 10850
 
9.1%
4.0 10366
 
8.7%
99.0 693
 
0.6%
(Missing) 22008
18.5%

Length

2023-10-05T00:45:10.255234image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:10.347255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 53966
55.6%
2.0 21205
 
21.8%
1.0 10850
 
11.2%
4.0 10366
 
10.7%
99.0 693
 
0.7%

Most occurring characters

ValueCountFrequency (%)
. 97080
33.3%
0 97080
33.3%
3 53966
18.5%
2 21205
 
7.3%
1 10850
 
3.7%
4 10366
 
3.6%
9 1386
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 194853
66.7%
Other Punctuation 97080
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 97080
49.8%
3 53966
27.7%
2 21205
 
10.9%
1 10850
 
5.6%
4 10366
 
5.3%
9 1386
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 97080
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 291933
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 97080
33.3%
0 97080
33.3%
3 53966
18.5%
2 21205
 
7.3%
1 10850
 
3.7%
4 10366
 
3.6%
9 1386
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 291933
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 97080
33.3%
0 97080
33.3%
3 53966
18.5%
2 21205
 
7.3%
1 10850
 
3.7%
4 10366
 
3.6%
9 1386
 
0.5%

W30
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing21082
Missing (%)17.7%
Memory size930.5 KiB
2.0
45278 
1.0
34260 
4.0
7313 
3.0
6507 
99.0
4648 

Length

Max length4
Median length3
Mean length3.0474257
Min length3

Characters and Unicode

Total characters298666
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row4.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 45278
38.0%
1.0 34260
28.8%
4.0 7313
 
6.1%
3.0 6507
 
5.5%
99.0 4648
 
3.9%
(Missing) 21082
17.7%

Length

2023-10-05T00:45:10.440278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:10.524296image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 45278
46.2%
1.0 34260
35.0%
4.0 7313
 
7.5%
3.0 6507
 
6.6%
99.0 4648
 
4.7%

Most occurring characters

ValueCountFrequency (%)
. 98006
32.8%
0 98006
32.8%
2 45278
15.2%
1 34260
 
11.5%
9 9296
 
3.1%
4 7313
 
2.4%
3 6507
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 200660
67.2%
Other Punctuation 98006
32.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98006
48.8%
2 45278
22.6%
1 34260
 
17.1%
9 9296
 
4.6%
4 7313
 
3.6%
3 6507
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 98006
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 298666
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 98006
32.8%
0 98006
32.8%
2 45278
15.2%
1 34260
 
11.5%
9 9296
 
3.1%
4 7313
 
2.4%
3 6507
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 298666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 98006
32.8%
0 98006
32.8%
2 45278
15.2%
1 34260
 
11.5%
9 9296
 
3.1%
4 7313
 
2.4%
3 6507
 
2.2%

WP21757
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)< 0.1%
Missing7522
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean1.7546833
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.5 KiB
2023-10-05T00:45:10.597312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8402276
Coefficient of variation (CV)0.47884857
Kurtosis13.994008
Mean1.7546833
Median Absolute Deviation (MAD)1
Skewness2.2788625
Sum195763
Variance0.70598241
MonotonicityNot monotonic
2023-10-05T00:45:10.677331image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 48147
40.4%
2 45422
38.1%
3 17315
 
14.5%
8 401
 
0.3%
4 182
 
0.2%
9 99
 
0.1%
(Missing) 7522
 
6.3%
ValueCountFrequency (%)
1 48147
40.4%
2 45422
38.1%
3 17315
 
14.5%
4 182
 
0.2%
8 401
 
0.3%
9 99
 
0.1%
ValueCountFrequency (%)
9 99
 
0.1%
8 401
 
0.3%
4 182
 
0.2%
3 17315
 
14.5%
2 45422
38.1%
1 48147
40.4%

WP21758
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing6515
Missing (%)5.5%
Memory size930.5 KiB
2.0
48019 
1.0
44547 
3.0
19719 
8.0
 
185
9.0
 
103

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters337719
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 48019
40.3%
1.0 44547
37.4%
3.0 19719
16.6%
8.0 185
 
0.2%
9.0 103
 
0.1%
(Missing) 6515
 
5.5%

Length

2023-10-05T00:45:10.766362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:10.875375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 48019
42.7%
1.0 44547
39.6%
3.0 19719
17.5%
8.0 185
 
0.2%
9.0 103
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 112573
33.3%
0 112573
33.3%
2 48019
14.2%
1 44547
 
13.2%
3 19719
 
5.8%
8 185
 
0.1%
9 103
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 225146
66.7%
Other Punctuation 112573
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 112573
50.0%
2 48019
21.3%
1 44547
 
19.8%
3 19719
 
8.8%
8 185
 
0.1%
9 103
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 112573
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 337719
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 112573
33.3%
0 112573
33.3%
2 48019
14.2%
1 44547
 
13.2%
3 19719
 
5.8%
8 185
 
0.1%
9 103
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 337719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 112573
33.3%
0 112573
33.3%
2 48019
14.2%
1 44547
 
13.2%
3 19719
 
5.8%
8 185
 
0.1%
9 103
 
< 0.1%

WP21759
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing6515
Missing (%)5.5%
Memory size930.5 KiB
2.0
71955 
1.0
20212 
3.0
20127 
8.0
 
174
9.0
 
105

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters337719
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 71955
60.4%
1.0 20212
 
17.0%
3.0 20127
 
16.9%
8.0 174
 
0.1%
9.0 105
 
0.1%
(Missing) 6515
 
5.5%

Length

2023-10-05T00:45:10.961394image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:11.051414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 71955
63.9%
1.0 20212
 
18.0%
3.0 20127
 
17.9%
8.0 174
 
0.2%
9.0 105
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 112573
33.3%
0 112573
33.3%
2 71955
21.3%
1 20212
 
6.0%
3 20127
 
6.0%
8 174
 
0.1%
9 105
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 225146
66.7%
Other Punctuation 112573
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 112573
50.0%
2 71955
32.0%
1 20212
 
9.0%
3 20127
 
8.9%
8 174
 
0.1%
9 105
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 112573
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 337719
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 112573
33.3%
0 112573
33.3%
2 71955
21.3%
1 20212
 
6.0%
3 20127
 
6.0%
8 174
 
0.1%
9 105
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 337719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 112573
33.3%
0 112573
33.3%
2 71955
21.3%
1 20212
 
6.0%
3 20127
 
6.0%
8 174
 
0.1%
9 105
 
< 0.1%

WP21760
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing6515
Missing (%)5.5%
Memory size930.5 KiB
2.0
46585 
1.0
44613 
3.0
21024 
8.0
 
232
9.0
 
119

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters337719
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 46585
39.1%
1.0 44613
37.5%
3.0 21024
17.7%
8.0 232
 
0.2%
9.0 119
 
0.1%
(Missing) 6515
 
5.5%

Length

2023-10-05T00:45:11.137439image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:11.251460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 46585
41.4%
1.0 44613
39.6%
3.0 21024
18.7%
8.0 232
 
0.2%
9.0 119
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 112573
33.3%
0 112573
33.3%
2 46585
13.8%
1 44613
 
13.2%
3 21024
 
6.2%
8 232
 
0.1%
9 119
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 225146
66.7%
Other Punctuation 112573
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 112573
50.0%
2 46585
20.7%
1 44613
 
19.8%
3 21024
 
9.3%
8 232
 
0.1%
9 119
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 112573
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 337719
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 112573
33.3%
0 112573
33.3%
2 46585
13.8%
1 44613
 
13.2%
3 21024
 
6.2%
8 232
 
0.1%
9 119
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 337719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 112573
33.3%
0 112573
33.3%
2 46585
13.8%
1 44613
 
13.2%
3 21024
 
6.2%
8 232
 
0.1%
9 119
 
< 0.1%

WP21761
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing6515
Missing (%)5.5%
Memory size930.5 KiB
2.0
49276 
1.0
41986 
3.0
20869 
8.0
 
285
9.0
 
157

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters337719
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 49276
41.4%
1.0 41986
35.3%
3.0 20869
17.5%
8.0 285
 
0.2%
9.0 157
 
0.1%
(Missing) 6515
 
5.5%

Length

2023-10-05T00:45:11.335495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:11.419503image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 49276
43.8%
1.0 41986
37.3%
3.0 20869
18.5%
8.0 285
 
0.3%
9.0 157
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 112573
33.3%
0 112573
33.3%
2 49276
14.6%
1 41986
 
12.4%
3 20869
 
6.2%
8 285
 
0.1%
9 157
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 225146
66.7%
Other Punctuation 112573
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 112573
50.0%
2 49276
21.9%
1 41986
 
18.6%
3 20869
 
9.3%
8 285
 
0.1%
9 157
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 112573
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 337719
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 112573
33.3%
0 112573
33.3%
2 49276
14.6%
1 41986
 
12.4%
3 20869
 
6.2%
8 285
 
0.1%
9 157
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 337719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 112573
33.3%
0 112573
33.3%
2 49276
14.6%
1 41986
 
12.4%
3 20869
 
6.2%
8 285
 
0.1%
9 157
 
< 0.1%

WP21768
Categorical

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing5507
Missing (%)4.6%
Memory size930.5 KiB
1.0
68533 
2.0
39277 
8.0
 
5440
9.0
 
331

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters340743
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 68533
57.5%
2.0 39277
33.0%
8.0 5440
 
4.6%
9.0 331
 
0.3%
(Missing) 5507
 
4.6%

Length

2023-10-05T00:45:11.499532image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:11.578546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 68533
60.3%
2.0 39277
34.6%
8.0 5440
 
4.8%
9.0 331
 
0.3%

Most occurring characters

ValueCountFrequency (%)
. 113581
33.3%
0 113581
33.3%
1 68533
20.1%
2 39277
 
11.5%
8 5440
 
1.6%
9 331
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 227162
66.7%
Other Punctuation 113581
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 113581
50.0%
1 68533
30.2%
2 39277
 
17.3%
8 5440
 
2.4%
9 331
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 113581
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 340743
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 113581
33.3%
0 113581
33.3%
1 68533
20.1%
2 39277
 
11.5%
8 5440
 
1.6%
9 331
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 340743
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 113581
33.3%
0 113581
33.3%
1 68533
20.1%
2 39277
 
11.5%
8 5440
 
1.6%
9 331
 
0.1%

age_mh
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing95121
Missing (%)79.9%
Infinite0
Infinite (%)0.0%
Mean4.3763508
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.5 KiB
2023-10-05T00:45:11.651548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum99
Range98
Interquartile range (IQR)2

Descriptive statistics

Standard deviation10.838861
Coefficient of variation (CV)2.4766892
Kurtosis71.425264
Mean4.3763508
Median Absolute Deviation (MAD)1
Skewness8.518728
Sum104888
Variance117.4809
MonotonicityNot monotonic
2023-10-05T00:45:11.724566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 7743
 
6.5%
2 6525
 
5.5%
4 4152
 
3.5%
5 4092
 
3.4%
1 1148
 
1.0%
99 307
 
0.3%
(Missing) 95121
79.9%
ValueCountFrequency (%)
1 1148
 
1.0%
2 6525
5.5%
3 7743
6.5%
4 4152
3.5%
5 4092
3.4%
99 307
 
0.3%
ValueCountFrequency (%)
99 307
 
0.3%
5 4092
3.4%
4 4152
3.5%
3 7743
6.5%
2 6525
5.5%
1 1148
 
1.0%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.121994
Minimum15
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.5 KiB
2023-10-05T00:45:11.814604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile18
Q126
median36
Q352
95-th percentile72
Maximum100
Range85
Interquartile range (IQR)26

Descriptive statistics

Standard deviation17.302497
Coefficient of variation (CV)0.43124719
Kurtosis-0.0059807941
Mean40.121994
Median Absolute Deviation (MAD)12
Skewness0.77618691
Sum4778048
Variance299.3764
MonotonicityNot monotonic
2023-10-05T00:45:11.924611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 4478
 
3.8%
25 3988
 
3.3%
20 3669
 
3.1%
35 3523
 
3.0%
28 3473
 
2.9%
40 3404
 
2.9%
22 3137
 
2.6%
23 3097
 
2.6%
27 3017
 
2.5%
24 3003
 
2.5%
Other values (76) 84299
70.8%
ValueCountFrequency (%)
15 760
 
0.6%
16 1003
 
0.8%
17 1247
 
1.0%
18 2950
2.5%
19 2532
2.1%
20 3669
3.1%
21 2652
2.2%
22 3137
2.6%
23 3097
2.6%
24 3003
2.5%
ValueCountFrequency (%)
100 631
0.5%
99 31
 
< 0.1%
98 1
 
< 0.1%
97 2
 
< 0.1%
96 1
 
< 0.1%
95 6
 
< 0.1%
94 10
 
< 0.1%
93 8
 
< 0.1%
92 14
 
< 0.1%
91 16
 
< 0.1%

age_var1
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
2
45349 
1
40033 
3
33075 
99
 
631

Length

Max length2
Median length1
Mean length1.0052986
Min length1

Characters and Unicode

Total characters119719
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 45349
38.1%
1 40033
33.6%
3 33075
27.8%
99 631
 
0.5%

Length

2023-10-05T00:45:12.035643image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:12.128658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2 45349
38.1%
1 40033
33.6%
3 33075
27.8%
99 631
 
0.5%

Most occurring characters

ValueCountFrequency (%)
2 45349
37.9%
1 40033
33.4%
3 33075
27.6%
9 1262
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119719
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 45349
37.9%
1 40033
33.4%
3 33075
27.6%
9 1262
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 119719
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 45349
37.9%
1 40033
33.4%
3 33075
27.6%
9 1262
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 45349
37.9%
1 40033
33.4%
3 33075
27.6%
9 1262
 
1.1%

age_var2
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
2
45349 
1
40033 
3
20244 
4
12831 
99
 
631

Length

Max length2
Median length1
Mean length1.0052986
Min length1

Characters and Unicode

Total characters119719
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row1
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 45349
38.1%
1 40033
33.6%
3 20244
17.0%
4 12831
 
10.8%
99 631
 
0.5%

Length

2023-10-05T00:45:12.219677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:12.313716image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2 45349
38.1%
1 40033
33.6%
3 20244
17.0%
4 12831
 
10.8%
99 631
 
0.5%

Most occurring characters

ValueCountFrequency (%)
2 45349
37.9%
1 40033
33.4%
3 20244
16.9%
4 12831
 
10.7%
9 1262
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119719
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 45349
37.9%
1 40033
33.4%
3 20244
16.9%
4 12831
 
10.7%
9 1262
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 119719
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 45349
37.9%
1 40033
33.4%
3 20244
16.9%
4 12831
 
10.7%
9 1262
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 45349
37.9%
1 40033
33.4%
3 20244
16.9%
4 12831
 
10.7%
9 1262
 
1.1%

age_var3
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
4
33075 
3
30876 
2
30456 
1
24050 
99
 
631

Length

Max length2
Median length1
Mean length1.0052986
Min length1

Characters and Unicode

Total characters119719
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row2
4th row4
5th row3

Common Values

ValueCountFrequency (%)
4 33075
27.8%
3 30876
25.9%
2 30456
25.6%
1 24050
20.2%
99 631
 
0.5%

Length

2023-10-05T00:45:12.408722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:12.503749image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
4 33075
27.8%
3 30876
25.9%
2 30456
25.6%
1 24050
20.2%
99 631
 
0.5%

Most occurring characters

ValueCountFrequency (%)
4 33075
27.6%
3 30876
25.8%
2 30456
25.4%
1 24050
20.1%
9 1262
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119719
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 33075
27.6%
3 30876
25.8%
2 30456
25.4%
1 24050
20.1%
9 1262
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 119719
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 33075
27.6%
3 30876
25.8%
2 30456
25.4%
1 24050
20.1%
9 1262
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 33075
27.6%
3 30876
25.8%
2 30456
25.4%
1 24050
20.1%
9 1262
 
1.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
1
60722 
2
58366 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119088
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 60722
51.0%
2 58366
49.0%

Length

2023-10-05T00:45:12.587768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:12.666796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1 60722
51.0%
2 58366
49.0%

Most occurring characters

ValueCountFrequency (%)
1 60722
51.0%
2 58366
49.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119088
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 60722
51.0%
2 58366
49.0%

Most occurring scripts

ValueCountFrequency (%)
Common 119088
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 60722
51.0%
2 58366
49.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 60722
51.0%
2 58366
49.0%

Education
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
2
65111 
3
37407 
1
15825 
99
 
745

Length

Max length2
Median length1
Mean length1.0062559
Min length1

Characters and Unicode

Total characters119833
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 65111
54.7%
3 37407
31.4%
1 15825
 
13.3%
99 745
 
0.6%

Length

2023-10-05T00:45:12.740802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:12.824820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
2 65111
54.7%
3 37407
31.4%
1 15825
 
13.3%
99 745
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2 65111
54.3%
3 37407
31.2%
1 15825
 
13.2%
9 1490
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119833
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 65111
54.3%
3 37407
31.2%
1 15825
 
13.2%
9 1490
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 119833
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 65111
54.3%
3 37407
31.2%
1 15825
 
13.2%
9 1490
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119833
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 65111
54.3%
3 37407
31.2%
1 15825
 
13.2%
9 1490
 
1.2%

Household_Income
Categorical

Distinct5
Distinct (%)< 0.1%
Missing1000
Missing (%)0.8%
Memory size930.5 KiB
5.0
32155 
4.0
25782 
3.0
22571 
2.0
19790 
1.0
17790 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters354264
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row4.0
3rd row4.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
5.0 32155
27.0%
4.0 25782
21.6%
3.0 22571
19.0%
2.0 19790
16.6%
1.0 17790
14.9%
(Missing) 1000
 
0.8%

Length

2023-10-05T00:45:12.921839image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:13.051867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
5.0 32155
27.2%
4.0 25782
21.8%
3.0 22571
19.1%
2.0 19790
16.8%
1.0 17790
15.1%

Most occurring characters

ValueCountFrequency (%)
. 118088
33.3%
0 118088
33.3%
5 32155
 
9.1%
4 25782
 
7.3%
3 22571
 
6.4%
2 19790
 
5.6%
1 17790
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 236176
66.7%
Other Punctuation 118088
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118088
50.0%
5 32155
 
13.6%
4 25782
 
10.9%
3 22571
 
9.6%
2 19790
 
8.4%
1 17790
 
7.5%
Other Punctuation
ValueCountFrequency (%)
. 118088
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 354264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 118088
33.3%
0 118088
33.3%
5 32155
 
9.1%
4 25782
 
7.3%
3 22571
 
6.4%
2 19790
 
5.6%
1 17790
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 354264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 118088
33.3%
0 118088
33.3%
5 32155
 
9.1%
4 25782
 
7.3%
3 22571
 
6.4%
2 19790
 
5.6%
1 17790
 
5.0%

Global11Regions
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9617762
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.5 KiB
2023-10-05T00:45:13.153888image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q310
95-th percentile11
Maximum11
Range10
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.7039276
Coefficient of variation (CV)0.62127921
Kurtosis-1.5226309
Mean5.9617762
Median Absolute Deviation (MAD)4
Skewness0.0062672797
Sum709976
Variance13.719079
MonotonicityNot monotonic
2023-10-05T00:45:13.242909image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
11 20122
16.9%
2 19096
16.0%
1 18025
15.1%
8 15029
12.6%
10 13181
11.1%
5 8527
7.2%
6 8027
 
6.7%
3 7002
 
5.9%
7 6067
 
5.1%
9 2011
 
1.7%
ValueCountFrequency (%)
1 18025
15.1%
2 19096
16.0%
3 7002
 
5.9%
4 2001
 
1.7%
5 8527
7.2%
6 8027
6.7%
7 6067
 
5.1%
8 15029
12.6%
9 2011
 
1.7%
10 13181
11.1%
ValueCountFrequency (%)
11 20122
16.9%
10 13181
11.1%
9 2011
 
1.7%
8 15029
12.6%
7 6067
 
5.1%
6 8027
 
6.7%
5 8527
7.2%
4 2001
 
1.7%
3 7002
 
5.9%
2 19096
16.0%

wbi
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size930.5 KiB
4
43201 
3
35665 
2
34179 
1
6043 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119088
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 43201
36.3%
3 35665
29.9%
2 34179
28.7%
1 6043
 
5.1%

Length

2023-10-05T00:45:13.330929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T00:45:13.418954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
4 43201
36.3%
3 35665
29.9%
2 34179
28.7%
1 6043
 
5.1%

Most occurring characters

ValueCountFrequency (%)
4 43201
36.3%
3 35665
29.9%
2 34179
28.7%
1 6043
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119088
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 43201
36.3%
3 35665
29.9%
2 34179
28.7%
1 6043
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 119088
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 43201
36.3%
3 35665
29.9%
2 34179
28.7%
1 6043
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 43201
36.3%
3 35665
29.9%
2 34179
28.7%
1 6043
 
5.1%

Subjective_Income
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2239604
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.5 KiB
2023-10-05T00:45:13.487964image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0392135
Coefficient of variation (CV)0.46728056
Kurtosis0.34874973
Mean2.2239604
Median Absolute Deviation (MAD)1
Skewness0.77011725
Sum264847
Variance1.0799647
MonotonicityNot monotonic
2023-10-05T00:45:13.559980image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 48684
40.9%
1 31301
26.3%
3 23127
19.4%
4 13976
 
11.7%
5 1107
 
0.9%
6 893
 
0.7%
ValueCountFrequency (%)
1 31301
26.3%
2 48684
40.9%
3 23127
19.4%
4 13976
 
11.7%
5 1107
 
0.9%
6 893
 
0.7%
ValueCountFrequency (%)
6 893
 
0.7%
5 1107
 
0.9%
4 13976
 
11.7%
3 23127
19.4%
2 48684
40.9%
1 31301
26.3%

EMP_2010
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.2732075
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size930.5 KiB
2023-10-05T00:45:13.625007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.1365197
Coefficient of variation (CV)0.65272969
Kurtosis-1.6994683
Mean3.2732075
Median Absolute Deviation (MAD)2
Skewness0.20126782
Sum389767
Variance4.5647165
MonotonicityNot monotonic
2023-10-05T00:45:13.693022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 42866
36.0%
6 35287
29.6%
2 15588
 
13.1%
5 9907
 
8.3%
4 8178
 
6.9%
3 7252
 
6.1%
(Missing) 10
 
< 0.1%
ValueCountFrequency (%)
1 42866
36.0%
2 15588
 
13.1%
3 7252
 
6.1%
4 8178
 
6.9%
5 9907
 
8.3%
6 35287
29.6%
ValueCountFrequency (%)
6 35287
29.6%
5 9907
 
8.3%
4 8178
 
6.9%
3 7252
 
6.1%
2 15588
 
13.1%
1 42866
36.0%

Interactions

2023-10-05T00:44:49.478287image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:34.108458image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:35.530779image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:36.961118image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:38.420436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:39.613068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:40.813340image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:42.174646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:43.542955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:44.757228image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:46.132539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:48.025953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:49.595320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:34.239499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:35.650817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:37.090129image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:38.522466image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:39.707083image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:40.930371image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:42.294678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:43.644966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:44.881250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:46.250565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:48.153989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:49.713341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:34.357513image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:35.775833image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:37.214170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:38.618480image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:39.822109image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:41.044379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:42.410704image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:43.741993image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:45.001278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:46.366585image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:48.285025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:49.819370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:34.467554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:35.894861image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:37.327196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:38.723510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:39.924126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:41.151409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:42.522727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:43.848011image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:45.109302image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:46.478604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:48.396046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:49.909378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:34.560565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:35.994890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:37.427218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:38.814532image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:40.026161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:41.243425image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:42.615732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:43.941052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:45.200330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:46.569638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:48.485065image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:50.020403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:34.679592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:36.119923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:37.549245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:38.920903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:40.119169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:41.371452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:42.729771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:44.039054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:45.313356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:46.681652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:48.603090image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:50.139429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:34.802615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:36.241954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:37.680275image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:39.018928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:40.220205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:41.485477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:42.847785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:44.134094image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:45.428380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:46.808679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:48.724119image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:50.246455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:34.905637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:36.352975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:37.794301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:39.122945image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:40.322215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:41.589514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:42.954809image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:44.240113image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:45.531398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:47.428831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:48.836135image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:50.388485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:35.029680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:36.479004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:37.927320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:39.224986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:40.416249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:41.705530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:43.075836image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:44.337128image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:45.648435image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:47.549845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:48.971166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:50.508532image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:35.150699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:36.599025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:38.054359image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:39.323991image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:40.503256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:41.821572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:43.193877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:44.432143image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:45.767457image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:47.664878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:49.100195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:50.626553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:35.276732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:36.718058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:38.181376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:39.422026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:40.594277image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:41.935592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:43.312889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:44.530166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:45.893478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:47.781910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:49.228225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:50.750574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:35.403748image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:36.843086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:38.312417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:39.523049image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:40.688311image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:42.050618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:43.437932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:44.633206image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:46.018500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:47.904926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-10-05T00:44:49.358253image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2023-10-05T00:45:13.890072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
WPID_RANDOMWGTPROJWTMH7BMH7B_2W28WP21757age_mhAgeGlobal11RegionsSubjective_IncomeEMP_2010FIELD_DATEW1W2W3W4W5AW5BW5CW5DW5EW5FW5GW6W7AW7BW7CW8W9W10W11AW11BMH2AMH2BW13W14W15W15_1AW15_1BW15_1CW15_1DW15_1EW15_2AW15_2BMH1MH3AMH3BMH3CMH3DMH4AMH4BMH5MH6MH7AMH7CMH8AMH8BMH8CMH8DMH8EMH8FMH8GMH8HMH9AMH9BMH9CMH9DMH9EMH9FMH9GMH9HW27W29W30WP21758WP21759WP21760WP21761WP21768age_var1age_var2age_var3GenderEducationHousehold_Incomewbi
WPID_RANDOM1.0000.0010.001-0.0110.005-0.006-0.000-0.0120.0030.003-0.0020.0030.0000.0020.0050.0000.0000.0000.0010.0010.0040.0000.0000.0000.0000.0000.0000.0000.0040.0030.0000.0020.0050.0050.0030.0060.0020.0000.0040.0040.0000.0050.0000.0000.0050.0030.0000.0000.0020.0000.0000.0000.0040.0020.0000.0110.0000.0080.0000.0050.0080.0000.0000.0000.0000.0100.0000.0000.0130.0180.0000.0100.0000.0000.0040.0000.0000.0000.0000.0020.0050.0060.0040.0040.0000.0000.000
WGT0.0011.0000.4450.0310.1190.0510.0160.0400.035-0.0650.1060.1290.0460.1100.0940.2280.0300.0190.0330.0440.0420.0270.0370.0310.0630.0540.0480.0480.0490.0500.0460.0490.0460.0670.0520.1080.0530.0330.0370.0330.0440.0330.0270.0350.0330.0470.0450.0440.0420.0470.0300.0360.0330.0230.0310.0250.0290.0270.0430.0340.0320.0180.0260.0240.0560.0300.0240.0180.0320.0240.0210.0370.1090.0280.0430.0470.0550.0470.0470.0230.0230.0280.0440.0260.2850.1060.082
PROJWT0.0010.4451.000-0.0300.1140.001-0.034-0.019-0.0470.1120.0850.0650.0790.0520.0750.0930.0170.0200.0230.0240.0410.0230.0280.0410.0180.0220.0220.0400.0410.0220.0340.0220.0210.0480.0350.0910.0370.0280.0180.0180.0330.0160.0140.0310.0200.0350.0290.0340.0190.0360.0420.0430.0470.0370.0380.0260.0390.0070.0150.0260.0000.0190.0330.0150.0190.0440.0080.0000.0360.0160.0250.0360.0790.0290.0340.0280.0430.0200.0270.0250.0170.0180.0220.0210.1330.0330.078
MH7B-0.0110.031-0.0301.000NaN0.127-0.0070.8960.627-0.1260.0390.0430.0440.0450.0490.0820.0400.0570.0430.0420.0390.0450.0320.0370.0370.0390.0390.0520.0560.0500.0680.0480.0530.0570.0520.0460.0370.0400.0390.0450.0430.0380.0420.0460.0430.0480.0410.0450.0430.0540.0390.0510.0510.0771.0000.1520.0880.0790.0590.1070.0640.0790.0920.0550.0460.0400.0290.0420.0350.0550.0450.0420.1410.0400.0550.0990.0970.0930.0930.0480.4270.4130.4130.0500.1020.0300.126
MH7B_20.0050.1190.114NaN1.0000.0620.0041.0000.224-0.0260.0350.1120.0510.0820.0000.1310.0320.0940.0830.0430.0630.0950.0510.0000.0660.0110.0710.0900.0790.0810.1210.0480.0390.0690.0670.1200.0510.0320.0990.1160.1000.0790.1120.0350.0690.0650.0620.1660.0360.1470.1240.0980.1040.0541.0000.3140.1690.1330.1580.1840.1130.1070.1610.1090.1330.1140.1240.0000.1370.1850.0990.1380.0740.0620.0000.1030.0750.0730.0540.0570.0530.0540.0530.0000.1830.0000.105
W28-0.0060.0510.0010.1270.0621.0000.0290.1390.216-0.0110.0700.0470.0260.0470.0550.0210.0420.0550.0340.0330.0480.0430.0430.0430.0330.0370.0380.0500.0520.0500.0420.0510.0430.0650.0530.0280.0560.0500.0520.0550.0570.0560.0450.0610.0550.0370.0600.0670.0660.0570.0660.0720.0590.0340.0330.0360.0000.0170.0050.0150.0270.0000.0310.0180.0000.0510.0000.0000.0270.0000.0160.0291.0000.1410.0370.0820.0690.0740.0600.0300.0230.0230.0230.0130.0520.0180.032
WP21757-0.0000.016-0.034-0.0070.0040.0291.000-0.0110.010-0.028-0.1180.0020.0310.0740.0800.0720.0620.0520.0580.0620.0590.0590.0630.0480.0690.0680.0720.0660.0730.0760.0430.0650.0520.0660.0620.1030.0590.0630.0690.0850.0690.0780.0580.0720.0700.1000.0830.0820.0650.0820.0880.0940.0700.1020.1170.0660.0460.0570.0840.0680.0640.0320.0430.0590.0520.0460.0580.0580.0450.0610.0490.0470.1440.0630.0590.2210.2200.1930.1810.1210.0360.0350.0320.0390.0970.0340.082
age_mh-0.0120.040-0.0190.8961.0000.139-0.0111.0000.648-0.1420.0350.0440.0240.0550.0550.0470.0200.0400.0480.0290.0410.0310.0450.0410.0340.0420.0470.0740.0670.0590.0600.0640.0600.0570.0480.0670.0540.0480.0630.0660.0690.0520.0560.0550.0470.0500.0590.0980.0520.0940.0700.0720.0740.0531.0000.2800.1280.1190.1070.1210.0960.0780.1020.0820.0690.0750.0450.0360.0730.0670.0600.0630.0520.0340.0340.0730.0560.0500.0440.0470.0460.0450.0470.0090.0990.0000.058
Age0.0030.035-0.0470.6270.2240.2160.0100.6481.000-0.339-0.0860.0810.0960.0310.0460.0860.0840.0920.0480.0730.0340.0820.0400.0280.0490.0400.0410.0330.0650.0600.0720.0400.0290.0220.0360.0910.0330.0200.0170.0550.0330.0440.0420.0490.0460.0700.0260.0320.0310.0330.0330.0450.0370.0370.0490.0120.1230.0450.0320.1610.0170.0550.0790.0370.0080.0250.0170.0240.0340.0420.0250.0460.1830.0490.0950.1760.1720.1700.1700.0460.9300.9170.9290.0490.1180.0380.247
Global11Regions0.003-0.0650.112-0.126-0.026-0.011-0.028-0.142-0.3391.0000.3090.0200.4060.1270.2060.1290.1470.1520.1320.1610.1100.1390.1160.1850.1370.1200.1120.1170.1420.1190.1060.0910.0880.1210.1100.2070.1130.1090.0960.1040.0860.0910.1350.1130.1390.1840.0830.0960.0790.0940.0800.0960.1510.1330.1180.0590.2200.2250.0690.1330.1150.1390.1320.1030.0930.1000.0830.1020.0760.0720.0940.1000.1370.0860.2140.1500.1530.1410.1520.1230.2380.2170.2100.0730.1370.0380.532
Subjective_Income-0.0020.1060.0850.0390.0350.070-0.1180.035-0.0860.3091.0000.0750.0780.1150.1270.1290.1200.1020.0770.1090.0590.1060.0900.0430.1080.0910.0860.0750.1090.1230.0560.0850.0760.0870.0650.1210.0790.0420.0670.0750.0570.0790.0470.0700.0850.1040.0750.0670.0690.0700.0540.0610.0600.0840.1290.0780.1060.1110.0740.0700.0620.0570.0500.0580.0310.0180.0280.0340.0420.0670.0410.0350.0940.0360.0990.1360.1590.1130.1430.0570.0700.0680.0640.0240.1570.1630.177
EMP_20100.0030.1290.0650.0430.1120.0470.0020.0440.0810.0200.0751.0000.0400.0530.0640.1000.0550.0530.0350.0500.0300.0490.0410.0320.0480.0470.0430.0410.0550.0510.0400.0360.0350.0450.0420.0710.0350.0260.0300.0410.0290.0350.0440.0400.0470.0650.0260.0330.0360.0350.0170.0230.0330.0570.0740.0330.0540.0810.0330.0600.0480.1080.0690.0380.0210.0050.0110.0220.0270.0510.0240.0200.1210.0250.0620.2840.2880.2870.2950.0170.1850.2140.1760.2150.1230.0960.134
FIELD_DATE0.0000.0460.0790.0440.0510.0260.0310.0240.0960.4060.0780.0401.0000.0470.0510.0410.0960.0550.0560.0750.0520.0910.0590.0670.0470.0530.0470.0500.0590.0620.0400.0620.0590.0610.0490.0700.0640.0390.0410.0440.0400.0550.0440.0460.0830.0540.0590.0450.0500.0500.0330.0440.0620.0630.0470.0240.0850.0500.0180.0480.0230.0220.0410.0210.0340.0490.0300.0320.0330.0210.0290.0390.0500.0270.0880.0540.0480.0470.0530.0630.0970.0920.0850.0230.0480.0100.265
W10.0020.1100.0520.0450.0820.0470.0740.0550.0310.1270.1150.0530.0471.0000.3120.2540.0990.0880.0760.1370.0900.0910.1040.0660.1780.1460.1260.1040.1290.1500.0810.1070.0940.1430.1210.2120.1730.0610.0830.0920.0950.0890.0630.0710.0820.1020.1070.1010.0980.1070.0770.0850.0690.0700.0610.0520.0820.0460.0310.0330.0490.0280.0360.0630.0410.0330.0470.0390.0400.0420.0390.0430.1330.0640.1010.0480.0570.0430.0550.0290.0320.0280.0290.0810.2390.0800.102
W20.0050.0940.0750.0490.0000.0550.0800.0550.0460.2060.1270.0640.0510.3121.0000.2340.0980.0910.0770.1510.0880.0990.1050.0680.1970.1620.1420.1050.1340.1550.0810.1100.0920.1660.1180.2330.1750.0660.0840.0970.0990.1020.0720.0810.1000.1410.1130.1030.1030.1080.0980.1030.0720.0960.0800.0480.0960.1120.0260.0320.0650.0590.0650.0650.0270.0430.0500.0420.0420.0520.0460.0590.1460.0570.1340.0580.0790.0530.0690.0340.0440.0390.0400.0180.2230.0680.168
W30.0000.2280.0930.0820.1310.0210.0720.0470.0860.1290.1290.1000.0410.2540.2341.0000.0500.0500.0530.0920.0600.0490.0660.0550.1420.1140.0930.0840.0770.0920.0550.0910.0730.1230.0980.1890.1210.0580.0610.0680.0740.0660.0690.0620.0850.1000.0740.0770.0770.0810.0510.0620.0550.0340.0400.0060.0620.0600.0110.0280.0360.0240.0230.0390.0280.0260.0270.0310.0280.0390.0310.0370.1650.0460.1270.0740.0950.0670.0810.0160.0610.0660.0760.0310.5060.1340.090
W40.0000.0300.0170.0400.0320.0420.0620.0200.0840.1470.1200.0550.0960.0990.0980.0501.0000.1770.2250.2350.1870.3690.1900.1190.1930.1750.1790.1760.1870.1910.0860.1210.1080.1350.1270.0820.0840.0490.1520.1340.1220.1820.0800.1030.0660.0710.1450.1230.1220.1090.0850.0860.0660.0550.0680.0380.1000.0320.0350.0740.0450.0300.0400.0220.0540.0390.0470.0690.0380.0530.0400.0370.0540.0430.0760.0640.0510.0560.0620.0930.0860.0810.0760.0310.0630.0230.164
W5A0.0000.0190.0200.0570.0940.0550.0520.0400.0920.1520.1020.0530.0550.0880.0910.0500.1771.0000.1690.2010.1780.2110.1940.1380.1580.1480.1570.1200.1340.1400.0650.0830.0820.1070.1060.0710.0720.0370.1060.1350.0910.1200.0880.0750.0590.0720.1000.1000.0970.0930.0650.0750.0810.0510.0690.0390.0690.0420.0550.0480.0390.0210.0500.0370.0370.0430.0600.0540.0350.0660.0510.0460.0420.0410.0670.0540.0530.0500.0560.0520.0980.0890.0870.0470.0590.0190.121
W5B0.0010.0330.0230.0430.0830.0340.0580.0480.0480.1320.0770.0350.0560.0760.0770.0530.2250.1691.0000.2140.2540.2290.2110.1450.1490.1570.1750.2940.1840.1750.1240.1080.1160.1130.1300.0660.0690.0570.2750.1370.1350.1380.1430.1400.0930.0670.1180.1240.1020.1150.0920.0920.0980.0650.0630.0420.0480.0510.0280.0470.0440.0450.0400.0300.0510.0520.0440.0680.0450.0460.0510.0370.0690.0400.0590.0390.0340.0340.0410.1090.0450.0450.0410.0510.0600.0200.115
W5C0.0010.0440.0240.0420.0430.0330.0620.0290.0730.1610.1090.0500.0750.1370.1510.0920.2350.2010.2141.0000.2400.2910.2430.1610.3050.2840.2760.1980.2560.2690.1120.1400.1330.1850.1700.1160.1040.0630.1520.1540.1540.1830.1020.1000.0860.0870.1610.1500.1520.1420.0960.1060.0850.0650.0530.0370.1110.0640.0330.0540.0530.0380.0580.0470.0580.0280.0470.0600.0380.0540.0630.0440.0540.0400.1150.0540.0500.0490.0610.0840.0780.0690.0680.0370.0930.0290.143
W5D0.0040.0420.0410.0390.0630.0480.0590.0410.0340.1100.0590.0300.0520.0900.0880.0600.1870.1780.2540.2401.0000.2170.2530.1680.1640.1810.1930.1960.1720.1720.1160.1060.1110.1320.1470.0950.0790.0670.1550.1340.1440.1340.1290.1240.0850.0750.1250.1310.1040.1250.0830.0950.1010.0570.0490.0240.0480.0370.0450.0490.0500.0470.0570.0290.0570.0410.0560.0710.0260.0490.0510.0330.0680.0500.0600.0340.0380.0310.0360.0910.0320.0300.0300.0600.0710.0320.076
W5E0.0000.0270.0230.0450.0950.0430.0590.0310.0820.1390.1060.0490.0910.0910.0990.0490.3690.2110.2290.2910.2171.0000.2240.1470.2140.2010.2080.1620.1810.1920.0830.1230.1030.1550.1280.0920.0850.0470.1510.1440.1360.2220.0840.0960.0660.0820.1580.1340.1320.1180.0970.0990.0700.0530.0650.0190.0970.0420.0370.0660.0390.0170.0420.0200.0790.0380.0690.0800.0480.0550.0540.0540.0530.0380.0790.0550.0470.0490.0560.0950.0820.0770.0730.0270.0590.0220.143
W5F0.0000.0370.0280.0320.0510.0430.0630.0450.0400.1160.0900.0410.0590.1040.1050.0660.1900.1940.2110.2430.2530.2241.0000.1700.1800.1920.2050.1790.1740.1820.1100.1100.1120.1400.1490.0950.0840.0640.1430.1300.1490.1420.1200.1120.0820.0740.1300.1360.1160.1330.0890.1060.0960.0660.0530.0210.0820.0260.0420.0540.0480.0470.0540.0360.0620.0380.0540.0630.0400.0550.0600.0460.0540.0470.0670.0420.0400.0390.0460.0670.0350.0350.0330.0450.0740.0300.107
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WP217680.0020.0230.0250.0480.0570.0300.1210.0470.0460.1230.0570.0170.0630.0290.0340.0160.0930.0520.1090.0840.0910.0950.0670.0350.0800.0900.0910.0930.0920.0950.0910.0930.0850.0570.0660.0350.0310.0510.0980.0900.1000.0980.0760.1060.0650.0350.0880.0870.0830.0780.0610.0680.0740.0600.0550.0280.0370.0360.0510.0610.0410.0480.0420.0250.0530.0040.0360.0690.0240.0240.0220.0250.0770.0280.0720.1120.1050.0960.1021.0000.0380.0450.0390.0650.0460.0160.061
age_var10.0050.0230.0170.4270.0530.0230.0360.0460.9300.2380.0700.1850.0970.0320.0440.0610.0860.0980.0450.0780.0320.0820.0350.0220.0520.0390.0430.0320.0580.0530.0660.0390.0250.0230.0350.0850.0360.0190.0150.0580.0330.0450.0460.0480.0470.0650.0250.0320.0320.0340.0340.0480.0340.0210.0430.0130.1170.0380.0200.1550.0020.0340.0730.0240.0000.0180.0090.0250.0240.0300.0190.0380.1420.0500.1020.1440.1410.1370.1370.0381.0001.0000.9390.0400.0810.0110.228
age_var20.0060.0280.0180.4130.0540.0230.0350.0450.9170.2170.0680.2140.0920.0280.0390.0660.0810.0890.0450.0690.0300.0770.0350.0230.0460.0350.0380.0310.0610.0550.0670.0390.0280.0210.0330.0860.0320.0180.0160.0530.0310.0410.0410.0450.0420.0660.0240.0300.0290.0310.0300.0420.0360.0320.0470.0130.1180.0420.0260.1560.0030.0470.0770.0260.0000.0270.0160.0230.0340.0340.0270.0400.1710.0470.0900.1620.1570.1550.1550.0451.0001.0000.8130.0420.0820.0160.237
age_var30.0040.0440.0220.4130.0530.0230.0320.0470.9290.2100.0640.1760.0850.0290.0400.0760.0760.0870.0410.0680.0300.0730.0330.0220.0460.0360.0380.0280.0600.0550.0690.0350.0230.0200.0300.0870.0310.0170.0140.0520.0290.0400.0400.0440.0420.0670.0220.0280.0290.0290.0310.0430.0340.0210.0430.0150.1190.0400.0220.1570.0100.0450.0720.0270.0000.0190.0100.0260.0240.0340.0160.0440.1420.0440.0900.1370.1350.1320.1320.0390.9390.8131.0000.0450.1100.0330.233
Gender0.0040.0260.0210.0500.0000.0130.0390.0090.0490.0730.0240.2150.0230.0810.0180.0310.0310.0470.0510.0370.0600.0270.0450.0140.0680.0620.0460.0550.0530.0440.0560.0350.0540.0250.0220.0220.0780.0440.0410.0210.0480.0300.0470.0420.0360.0350.0140.0220.0450.0330.0360.0590.0120.0290.0380.0330.0590.0000.0250.0520.0150.0760.0230.0480.0300.0370.0070.0080.0260.0100.0210.0270.0230.0580.0210.1510.1470.1530.1510.0650.0400.0420.0451.0000.0110.0830.030
Education0.0000.2850.1330.1020.1830.0520.0970.0990.1180.1370.1570.1230.0480.2390.2230.5060.0630.0590.0600.0930.0710.0590.0740.0530.1300.1140.0930.0930.0860.0980.0640.1000.0790.1310.1010.1960.1210.0690.0740.0790.0830.0760.0760.0720.0920.1050.0850.0870.0850.0910.0650.0770.0630.0540.0630.0300.0660.0730.0340.0530.0560.0280.0410.0540.0200.0350.0310.0350.0330.0260.0210.0350.1730.0570.1300.1200.1360.1140.1210.0460.0810.0820.1100.0111.0000.1520.102
Household_Income0.0000.1060.0330.0300.0000.0180.0340.0000.0380.0380.1630.0960.0100.0800.0680.1340.0230.0190.0200.0290.0320.0220.0300.0320.0530.0430.0340.0300.0300.0420.0350.0400.0300.0550.0400.0720.0500.0300.0280.0240.0320.0250.0300.0260.0340.0370.0330.0310.0430.0370.0220.0240.0190.0210.0480.0280.0270.0240.0150.0210.0280.0100.0030.0080.0350.0150.0140.0030.0300.0380.0270.0150.0700.0160.0370.0540.0720.0490.0570.0160.0110.0160.0330.0830.1521.0000.030
wbi0.0000.0820.0780.1260.1050.0320.0820.0580.2470.5320.1770.1340.2650.1020.1680.0900.1640.1210.1150.1430.0760.1430.1070.0740.1020.0930.0900.1080.1220.1190.0810.1080.0830.0840.0660.1740.1020.0550.0860.0990.0630.1030.1070.1280.1610.1670.0820.0750.0740.0850.0480.0760.0760.0450.0440.0240.1960.1480.0090.1000.0180.0430.0810.0540.0420.0620.0430.0600.0310.0250.0330.0610.0800.0620.1960.1470.1350.1280.1360.0610.2280.2370.2330.0300.1020.0301.000

Missing values

2023-10-05T00:44:51.157661image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-05T00:44:52.295917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-05T00:44:55.086546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

COUNTRYNEWWPID_RANDOMWGTPROJWTFIELD_DATEYEAR_WAVEW1W2W3W4W5AW5BW5CW5DW5EW5FW5GW6W7AW7BW7CW8W9W10W11AW11BMH2AMH2BW13W14W15W15_1AW15_1BW15_1CW15_1DW15_1EW15_2AW15_2BMH1MH3AMH3BMH3CMH3DMH4AMH4BMH5MH6MH7AMH7BMH7B_2MH7CMH8AMH8BMH8CMH8DMH8EMH8FMH8GMH8HMH9AMH9BMH9CMH9DMH9EMH9FMH9GMH9HW27W28W29W30WP21757WP21758WP21759WP21760WP21761WP21768age_mhAgeage_var1age_var2age_var3GenderEducationHousehold_IncomeGlobal11RegionswbiSubjective_IncomeEMP_2010
0United States1782168980.8034402.115603e+0510/01/20202020212.02.024.024.02223332.0222112411.03.02.02.04.02.02.02.02.022.04.02.04.02.02.02.022.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN16.03.02.01.02.02.02.02.02.0NaN80344225.09426.0
1United States1591070180.7295901.921143e+0510/01/20202020113.01.042.012.01221114.0111111111.02.04.02.01.01.04.03.03.021.01.01.01.01.02.02.012.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.03.0NaN1.02.02.01.01.01.0NaN23111134.09411.0
2United States2031664170.8485592.234410e+0510/01/20202020113.01.023.011.01341113.0111111211.01.03.02.01.01.03.02.03.021.02.01.02.01.02.02.022.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12.02.0NaN2.02.02.02.02.01.0NaN29112134.09411.0
3United States2010617190.3410838.981342e+0410/01/20202020213.02.024.012.02222214.0112332213.01.04.02.01.01.099.02.04.021.03.01.03.01.01.02.012.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN16.04.01.01.01.02.02.01.01.0NaN60334234.09421.0
4United States1954041620.9959332.622472e+0510/01/20202020312.01.044.011.01211114.0113131211.01.03.02.01.01.03.01.04.021.02.01.02.02.02.02.012.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN11.03.0NaN2.02.02.02.02.01.0NaN41223122.09446.0
5United States2061751660.3069088.081454e+0410/01/20202020212.01.022.014.01231112.0111131112.03.02.02.02.01.02.01.03.022.02.01.04.01.02.03.012.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15.03.04.02.01.02.01.02.01.0NaN68344224.09411.0
6United States1434530610.5615871.478761e+0510/01/20202020442.03.044.043.04444444.033223442NaNNaN2.04.04.02.02.03.04.013.04.04.04.01.01.02.022.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaN2.03.03.03.03.03.02.0NaN76344221.09416.0
7United States1512198290.7442161.959656e+0510/01/20202020322.02.023.014.01221112.0213111113.02.01.01.04.01.03.03.02.011.02.01.02.01.01.02.022.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaN2.01.02.02.02.02.01.0NaN71344124.09416.0
8United States1161757820.4281371.127361e+0510/01/20202020213.01.034.011.01241114.0112111111.01.04.01.01.01.02.01.04.021.02.01.02.01.01.02.022.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2NaNNaN3.02.02.02.02.02.01.0NaN71344235.09416.0
9United States2065936434.0461921.065436e+0610/01/20202020212.01.022.014.01221122.0211121112.02.02.02.02.01.02.03.04.021.02.02.02.01.01.02.022.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN11.02.0NaN1.01.02.01.02.02.0NaN36223123.09421.0
COUNTRYNEWWPID_RANDOMWGTPROJWTFIELD_DATEYEAR_WAVEW1W2W3W4W5AW5BW5CW5DW5EW5FW5GW6W7AW7BW7CW8W9W10W11AW11BMH2AMH2BW13W14W15W15_1AW15_1BW15_1CW15_1DW15_1EW15_2AW15_2BMH1MH3AMH3BMH3CMH3DMH4AMH4BMH5MH6MH7AMH7BMH7B_2MH7CMH8AMH8BMH8CMH8DMH8EMH8FMH8GMH8HMH9AMH9BMH9CMH9DMH9EMH9FMH9GMH9HW27W28W29W30WP21757WP21758WP21759WP21760WP21761WP21768age_mhAgeage_var1age_var2age_var3GenderEducationHousehold_IncomeGlobal11RegionswbiSubjective_IncomeEMP_2010
119078Kosovo1616033620.131784164.36905512/01/20202020222.02.024.032.03242223.0322332211.01.03.03.02.02.02.02.02.012.02.02.02.01.01.01.022.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.01.02.01.01.02.01.01.02.0NaN40223135.02321.0
119079Kosovo1972408261.6697632082.62730312/01/20202020211.02.014.033.02343332.0212113213.02.03.03.099.03.02.01.01.012.02.02.04.099.01.01.012.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN12.01.01.01.01.01.01.01.02.0NaN32222111.02323.0
119080Kosovo1178615260.323296403.23400912/01/20202020223.01.013.011.01142233.0332112211.01.04.04.01.01.04.01.01.011.04.01.01.01.01.03.022.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.01.01.02.02.02.02.02.01.0NaN58334135.02311.0
119081Kosovo2082596550.718126895.68906312/01/20202020112.04.022.0993.03213124.0332322112.01.02.04.01.03.02.04.04.014.02.02.04.01.02.03.022.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN11.03.02.02.02.02.02.01.02.0NaN25112133.02321.0
119082Kosovo1781557210.381181475.43179512/01/20202020223.03.023.012.03141122.0111112212.02.02.02.01.01.02.02.01.021.02.01.01.01.01.03.022.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.03.01.02.01.02.01.02.02.0NaN42223135.02321.0
119083Kosovo2010300630.250480312.41403112/01/20202020223.02.022.022.02242113.0322411114.01.01.01.01.01.02.02.02.021.01.01.01.01.01.02.0992.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.03.02.03.02.02.02.02.02.0NaN27112235.02311.0
119084Kosovo1175282350.188637235.27933712/01/20202020212.01.012.011.01122222.0991333222NaNNaN1.099.01.099.01.01.01.022.04.01.02.01.01.03.011.018.0NaN1.01.02.02.02.01.02.02.01.01.0NaNNaNNaN1.0NaNNaN1.02NaNNaN2.03.03.03.03.03.01.02.024111233.02326.0
119085Kosovo1520500100.191664239.05475712/01/20202020113.03.034.032.03132414.0122111211.03.04.02.02.02.02.04.01.011.01.01.01.02.01.03.022.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.03.02.01.01.02.01.01.01.0NaN24111135.02312.0
119086Kosovo1111839780.173042215.82763012/01/20202020313.03.024.042.022212994.0232222412.01.02.02.01.02.04.04.01.013.03.02.01.01.01.02.022.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.02.02.02.01.01.01.01.02.0NaN29112135.02321.0
119087Kosovo1869488310.131784164.36905512/01/20202020123.03.034.023.03241113.0322132211.02.02.01.01.01.03.03.01.021.02.01.01.01.02.02.012.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN13.03.01.02.01.02.01.02.01.0NaN30222135.02321.0